Objectives

• To outline improved methods for measuring performance in secondary care settings, which are central to hospital quality improvement, linking performance measurement with related organizational models

• To identify which and to which extent organizational/contextual factors impact the benefit generated by health technologies

• To produce a standardized method to measure the clinical variability with respect to the different organizational models in secondary care settings based on evidence from a selection of therapeutic areas

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Methodology

• Based on a prior extensive literature review, multiple case studies and quantitative methods (e.g. regression analysis) will be applied to identify a minimum set of indicators capturing variability in hospital performance

• A pilot study involving a network of collaborating hospitals will validate the methodology and the tool developed through a multiple case study approach

• Based on this, and building on AdHopHTA (www.adhophta.eu), a standard approach to assess organizational factors affecting health technologies’ effectiveness and the subsequent variability in the cost of care will be inferred

• Literature research and analysis on relationship between clinical decision making and organizational factors

• Analysis of two case studies: (1) Diagnostic use of high sensitivity troponin and its impact on NSTEMI myocardial infarction (Non-ST elevation myocardial infarction) diagnosis and treatment; (2) Use of robotic surgery with Da Vinci Robot

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Outcomes

• Toolkit including a list of relevant indicators to capture hospital performance variability and standard methodology to identify key organizational variables responsible for hospital performance
   

• Toolkit to assess the transferability of evidence produced in other jurisdictions and decision-making levels
   

• Toolkit to identify the clinical variability and its impact on the use of health technology compared to purely organizational variables, which will include a checklist to appraise the variability and a projection model on clinical outcomes
   

Outputs

• Carini E, Gabutti I, Frisicale EM, Di Pilla A, Pezzullo AM, De Waure C, Cicchetti A, Boccia S, Specchia ML (2020) Assessing hospital performance indicators. What dimensions? Evidence from an umbrella review. BMC Health Services Research. https://doi.org/10.1186/s12913-020-05879-y
   

• Grossi A, Hoxhaj I, Gabutti I, Specchia ML, Cicchetti A, Boccia S, De Waure C (2021) Hospital contextual factors affecting the implementation of health technologies: a systematic review. BMC Health Services Research. https://doi.org/10.1186/s12913-021-06423-2
   

•  Toolkit to capture hospital performance variability. [excel document]
   

• Toolkit to assess the transferability of evidence produced in other jurisdictions and decision-making levels. [PDF]

Leads

Università Cattolica del Sacro Cuore

 

Istituto Superiore di Sanita 

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Principal Investigators

Americo Cicchetti

Marco Marchetti

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