Ex vivo tissue modelling informs drug selection for rare cancers.

The identification and therapeutic targeting of actionable gene mutations across many cancer types has resulted in improved response rates in a minority of patients. The identification of actionable mutations is usually not sufficient to ensure complete nor durable responses, and in rare cancers, where no therapeutic standard of care exists, precision medicine indications are often based on pan-cancer data. The inclusion of functional data, however, can provide evidence of oncogene dependence and guide treatment selection based on tumour genetic data. We applied an ex vivo cancer explant modelling approach, that can be embedded in routine clinical care and allows for pathological review within 10 days of tissue collection. We now report that ex vivo tissue modelling provided accurate longitudinal response data in a patient with BRAFV600E -mutant papillary thyroid tumour with squamous differentiation. The ex vivo model guided treatment selection for this patient and confirmed treatment resistance when the patient's disease progressed after 8 months of treatment.

N1S3: a revised staging system for head and neck cutaneous squamous cell carcinoma with lymph node metastases: results of 2 Australian Cancer Centers.

BACKGROUND: A staging system was designed for metastatic cutaneous squamous cell carcinoma (SCC) that would incorporate the parotid as a regional level and facilitate a better prognostic discrimination between subgroups. METHODS: A retrospective review of clinical and pathological information of patients treated for metastatic cutaneous SCC to the parotid and/or neck was conducted. Potential prognostic factors were analyzed using univariate and multivariate analyses. A staging system was elaborated and externally validated. RESULTS: Two hundred fifteen patients were included. All patients had surgery as their primary treatment; 148 had parotidectomy with neck dissection, 50 parotidectomy alone, and 18 neck dissection alone. One hundred seventy-five patients received postoperative radiotherapy. On univariate analysis, the number of involved lymph nodes (P < .001), maximal size (P = .01), and extracapsular spread (P = .003) were found to be significant predictors of survival. On Cox regression, the number of involved lymph nodes as single or multiple (P = .006) was significant. The N1S3 staging system incorporates involved lymph nodes from parotid and neck (single or multiple) and the size (< or >3 cm). This system demonstrates significant predictive capacity for locoregional control (P < .001), disease-specific survival (P<.0001), and overall survival (P<.0001). N1S3 was tested on a different cohort of 250 patients, and the results confirmed those obtained from our primary analyses. CONCLUSIONS: The N1S3 system stages patients according to the number of involved lymph nodes and size, and incorporates parotid as 1 of the regional levels. These 2 predictors are easily applied on both clinical and pathological data.

Strengthening the revenue cycle: a 4-step method for optimizing payment.

Four steps for enhancing the revenue cycle to ensure optimal payment are: *Establish key performance indicator dashboards in each department that compare current with targeted performance; *Create proper organizational structures for each department; *Ensure that high-performing leaders are hired in all management and supervisory positions; *Implement efficient processes in underperforming operations.

Eyes on the supplies: results of a massive performance improvement initiative.

The University of Utah Hospitals & Clinics learned three lessons from its performance improvement initiative: Develop and track key performance indicators and compare them with performance targets on a monthly basis. Identify and define core processes and subprocesses, and build an appropriate organizational structure to support those processes. Identify supply cost-savings opportunities by focusing first on procurement.

Unlocking hospital gridlock.

Hospitals should identify barriers to timely patient discharges. Benchmarking hospital data with best-practice hospital experience can help in developing recommendations for improvement. Solutions include developing revised discharge policies and procedures, establishing and communicating a discharge time, and setting a deadline for physicians to write orders for discharge and discharge-dependent tests.

Improving hospital budgeting and accountability a best practice approach.

Best practices in setting and managing healthcare organization budgets include: Using comparative benchmarks. Setting accurate, high-performance department budgets. Establishing a culture of accountability. Managing expenses. Monitoring variances and requiring corrective action plans. Employing a balanced scorecard.