Conventional training is augmented with super high-fidelity patient simulations

Computer technology combines with medical science to construct realistic patient simulations. The simulators could be fully programmable to represent a variety of conditions, allowing medical students to practice on acutely ill ‘patients’ and develop their clinical skills in a risk-free environment.

Patient simulations can replicate illnesses and conditions to a high degree of accuracy and fidelity. This will transform medical training and the testing of new pharmaceuticals and new procedures.

High fidelity simulations can provide a safe environment which can be tailored to the education and training of students. Eder-Van Hook writes in Building a National Agenda for Simulation-Based Medical Education (2004): ‘Deaths due to preventable adverse events exceed deaths attributable to motor vehicle accidents, breast cancer, or AIDS’. Procedures, communication, diagnoses, treatments, etc. can all be learned without negatively impacting on patient safety. They provide the opportunity for highly interactive and experiential learning, ultimately enhancing the education process and improving outcomes for patients.

Headway is also being made in patient-specific surgery simulation. Companies, such as Simbionix, have already brought out patient-specific products, such as the PROcedure Rehersal Studio which has received U.S. Food and Drug Administration clearance for two applications: carotid stenting and endovascular aneurysm repair.

Current simulation models range from life-like mannequins, to interactive models, to computer simulations. As technological improvements progress, combinations of these can be used to construct increasingly realistic patient simulations able to represent a huge variety of conditions and react to treatment. These models can also be used for testing of new technologies, procedures and drugs.

This could speed up the training pathway as medical students would be able to practice on simulators if there are not enough patients or subjects. Additionally, it could speed up the time it takes for new drugs to come to market, by using fast, realistic and repeated simulation-based testing.

Analysis

This section outlines the projected workforce impacts of the idea.

Impact on sector

• Healthcare

workforce supply

[4,5,5,5]

workforce demandThe vertical axis represents an increase or decrease in supply or demand from the baseline which is the year 2012.

[4,4,3,3]

Level of confidence in this analysisThe confidence level is based on the confidence of the stakeholder who added this idea in their information.

Moderate

Moderate

Impact on staff

• General practitioners

workforce supply

[4,5,5,5]

workforce demandThe vertical axis represents an increase or decrease in supply or demand from the baseline which is the year 2012.

[4,4,3,3]

Level of confidence in this analysisThe confidence level is based on the confidence of the stakeholder who added this idea in their information.

Moderate

Moderate

Impact on quality of care This is calculated based on the analysis below. An increase receives a score of 1

Overall impact on quality of care: Positively impacts

SAFETY		Potential increase
EFFECTIVENESS		Potential increase
EXPERIENCE		Potential increase

Level of confidence in this analysis: High

Impact on financial outcomesThe scale used to assess the impact on financial outcomes is:
significant cost savings (25%+),
moderate cost savings (5%-25%),
minor costs savings (1%-5%),
minor increase in cost (1%-5%),
moderately increases cost (5%-25%),
significantly increases costs (25%+).

General public and service users		minor increase in cost (1%-5%)
Health and social care practitioners		minor costs savings (1%-5%)
Employers		minor increase in cost (1%-5%)
Training and education providers		minor increase in cost (1%-5%)
Private sector organisations		minor costs savings (1%-5%)
Commissioners		has no impact on costs
Government (England)		has no impact on costs

Geographical variance of impacts

Decrease imbalances

Level of certainty about impactsThe scale used in this question is:
Very likely (90–100% probability),
Likely (66–90% probability),
About as likely as not (33–66% probability),
Unlikely (10–33% probability),
Very unlikely (0–10% probability).

About as likely as not (33 to 66% probability)

Early signals

This section highlights any warning signals indicating that this idea is unfolding.

• New technologies coming to market that may improve simulation capabilities


• Developments in computing power, such as quantum computing