Will the threshold for seeking medical help for cancer symptoms reduce with better education?

Implementing a system that uses predictive models to replace traditional waiting lists with immediate treatment for conditions like cancer represents an ambitious shift towards a more proactive and efficient healthcare delivery model. Such a system aims to prioritize treatment based on the predicted urgency and benefit to the patient, rather than on a first-come, first-served basis. This approach could potentially improve outcomes by ensuring timely care for those who need it most. However, there are several factors and challenges to consider, including the cost implications of such a system. Yes, it is theoretically possible for a health system to leverage predictive modelling to significantly reduce or even eliminate waiting lists and backlogs for cancer treatment through more dynamic and data driven resource planning and care delivery. However, implementing such a system at scale would entail considerable costs. Some key elements to analyse would be:

Staffing costs: Predictive models could indicate optimal staff increases/decreases by cancer type and stage, but expanding specialized oncology staff capacity (physicians, nurses, technicians) requires major financial investment in recruitment and training.

Facility costs: If modelling shows a major boost in chemotherapy infusion, radiation or surgery capacity is needed, the hospital may have to make an immediate investment. Replacing waiting lists entirely with immediate treatment based on predictive models is a complex idea with both potential benefits and drawbacks. Predictive models could identify individuals at high risk for needing treatment sooner, facilitating early intervention and potentially better outcomes. By predicting demand, resources could be allocated more logically. Developing accurate predictive models requires vast amounts of data, including detailed health records, outcomes data, and possibly genetic information. These models would need to be highly accurate in assessing the severity of conditions and the likely benefit of immediate versus delayed treatment.

Healthcare infrastructure: Transitioning to a system based on immediate treatment necessitates significant changes in healthcare infrastructure and operations. This includes having the capacity to provide immediate treatment—which implies available healthcare professionals, treatment facilities, and medical equipment—and a flexible scheduling system that can adapt to the dynamic prioritization of cases. Significant upfront investment in AI and machine learning technology, data infrastructure, and training for healthcare providers will need capital funds for facility expansion - new operating rooms, infusion chairs, imaging, inpatient beds. Additional care coordinators, patient navigators, appointment schedulers may be needed to support a higher throughput system with reduced waiting lists for seamless patient transitions. More administrative workers will add to personnel costs.

AI and modelling costs: There would be substantial one-time and ongoing investments in AI tools, electronic health record integrations, data analysts, data governance – essential to building and sustaining predictive models to drive real-time decision-making. The overall cost expenditures could be offset by the health and economic outcomes of more patients treated in earlier disease stages, benefitting both individuals and populations. But the upfront cost lift in staff, facilities, infrastructure and technology to implement an AI-powered dynamic cancer treatment system would require major capital from payers, health systems, or government funds. Average costs would depend significantly on the scale, systems impacted, and pace of digital transformation. efficiently, reducing wasted capacity and improving overall system efficiency.

Challenges and limitations: Predictive models are not perfect, and inaccurate predictions could lead to unnecessary treatment or missed procedures. Triage based on predicted needs raises ethical concerns about fairness and resource allocation, especially for high-risk populations. Implementing such a system could be expensive, requiring significant changes to healthcare infrastructure and potentially increasing overall costs. Relying solely on predictions could lead to unnecessary interventions and potential harm to patients with low actual risk. While predictive models are used in healthcare settings for risk assessment and resource allocation, replacing waiting lists completely remains theoretical. Several pilot projects explore using AI in triage and scheduling, but ethical and practical concerns prevail.

Cost considerations: Increased operational costs to maintain the flexibility required for immediate treatment, including potentially higher staffing levels and the use of advanced diagnostic and treatment technologies. Potential cost savings over time due to improved health outcomes, reduced hospital stays, and possibly lower overall treatment costs due to early intervention. By prioritizing patients based on need and predicted outcomes, the system could reduce unnecessary treatments and focus resources on interventions with the highest expected benefit, potentially leading to more efficient use of healthcare resources. Immediate treatment, particularly for conditions like cancer, can often be less intensive (and less expensive) than treatment at more advanced stages.

Infrastructure changes: Integrating the system with existing healthcare systems might require costly infrastructure upgrades. Early intervention might decrease costs in the long run, but potential overdiagnosis and unnecessary treatment could offset the gains. Conclusion: While using predictive models for improved healthcare resource allocation shows promise, replacing waiting lists with immediate treatment based solely on predictions remains unlikely in the near future. Addressing ethical concerns, ensuring model accuracy, and managing costs are crucial before widespread implementation. savings despite the initial investment and higher operational costs. The use of predictive models can facilitate more personalized care, which might be more cost-effective in the long run by avoiding one-size-fits-all treatments that may be less effective.

Challenges and considerations: Ensuring that the system is fair and does not inadvertently prioritize patients based on socioeconomic status, race, or other unrelated factors is crucial. The predictive models must be highly accurate and transparent to gain the trust of both healthcare providers and patients. Managing and analysing large amounts of personal health data raises significant privacy concerns that must be addressed.

Conclusion: While theoretically possible, replacing waiting lists with a predictive AI model-based system for immediate treatment presents considerable logistical, ethical, and financial challenges. The cost implications are complex, involving significant initial investments and operational costs, but potentially offering efficiency gains and cost savings in the long term through improved health outcomes and more efficient resource use. Successful implementation would require careful planning, robust technology solutions, and a commitment to addressing ethical and financial issues.