Background: Healthcare systems worldwide are increasingly unable to meet the growing demand for and cost of healthcare. The primary problem is multiplied by changing demographics leading to increasing demand for services of increasing cost. It is exacerbated by the inability of the healthcare sector to realise (all of) the significant productivity gains seen in other sectors over the last 30 years. The end result is that healthcare costs an increasingly unaffordable share of national budgets that are increasingly constrained economically.
More specifically, an average 10% of healthcare costs are for acute and intensive care, which equates ~1% of GDP in many EU countries. Demand for intensive care is increasing demographically along with cost, while productivity is effectively flat. Demand is expected to continue growing demographically for the next 20 years exacerbated by increasing expectations of patients.
Highly trained doctors and nurses are the scarce and costly resource in critical and acute care. Thus, improving care and productivity in intensive and acute care units (ICUs) by merging engineering, technology and medicine presents a significant research and economic opportunity and challenge.
The Specific Problem: While acute and critical care doctors have a range of technology and sensors at their disposal, their ability to provide the more consistent, patient-specific care required to improve productivity and patient outcomes is limited. In particular, they are unable to take full advantage of the wealth of data they are presented to provide the best care. As a result, care is often variable and not patient-specific or as effective as it could be. More specifically, the mental models and experience these doctors use to process clinical data and make critical decisions about patient care are unable to bridge the gap to better productivity, patient-specific care and improved outcomes.
The Solution: The application of validated computer models of patient physiology that can be made patient-specific using data at their bedside can integrate patient data into a clear physiological picture of patient-specific condition and response to treatment, as well as provide suggestions and protocols to guide therapy. These computer models can be combined with automation technology to improve the productivity and quality of care and help alleviate demand.
Specifically, Model-based Therapeutics (MBT) which combines computer models of human physiology, clinical data and automation to solve clinical problems in diagnosis and treatment selection, as well as enabling the (hardware + software) automation of basic elements in the delivery of that care.
For further details see the Project Objectives!