Innovations in medical technologies are one of the leading areas of economic growth in the world. Whether new technologies take the form of pharmaceutical, medical device, biotechnology, information technology of some combination of these innovations, the opportunities for both private enterprise and social welfare are substantial. However, these innovations are not without cost, and require reimbursement from either a privately or publicly financed health care delivery system to enter the marketplace. This course aims to provide knowledge of the concepts, data, and methodology required to critically evaluate new medical technologies in order to secure financial investment, reimbursement, and regulatory compliance objectives, such as FDA approval. The course is designed to provide understanding of the analytic tools needed to evaluate medical technologies. After completing this course, students will have the skills needed to: - Understand the reimbursement systems financing medical technology use. - Understand the role of government and regulatory agencies in the development and use of new medical technologies. - Identify a population to be served by a medical technology. - Use health care data to evaluate a medical technology. - Perform cost/benefit and cost/effectiveness analysis of a new technology.
4.2.3 Decision Models
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Medical Technology and Evaluation
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Methodological Approaches and Considerations
Module 4 brings everything together from the first three modules. We will go into more detail about some of the more common quantitative methodologies you may encounter with an evaluation. We will talk about incorporating the effects of uncertainty and time into a medical technology evaluation model as well as the type of models that allow for the dynamic nature of patient responses to medical technology.
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Eric BarretteDirector of Research
Health Care Cost Institute
This is lesson 4.2.3, Decision Models.
Decision modelling is a means of incorporating probabilities and
future costs into a medical technology assessment.
Both base rule and discounting are tools that maybe useful for
calculating a decision model.
It is also possibly more useful to consider decision modelling in the case
of an example.
So consider a health care worker who has a needle-stick injury and whether or
not that health care worker should be given an HIV preventive treatment.
HIV is an incurable chronic illness, and
there is a risk of infection from a needle-stick injury.
Preventive treatment or prophylaxis treatment can be given to prevent HIV
infection, but there are many potential side effects and is not 100% effective.
Decision modeling is one means of determining whether or
not treatment is cost effective.
This slide exemplifies the decision model graphically.
The question being asked, should treatment be given?
Has two possible outcomes.
Yes, treatment should be given or no, treatment should not be given.
If treatment is given, there's a possibility of side effects but
there is also a possibility of no side effects.
In either case, because the treatment is not 100% effective,
there's also a probability of the healthcare worker still
getting HIV, or potentially not getting HIV.
If no treatment is given, there is also the probability of a healthcare worker
not getting HIV, but there is a possibility that they will get HIV.
All of these possibilities are depicted in this model.
In this case, the objective of our example technology assessment
is to determine if there are sufficient benefits to treatment for HIV for
the healthcare worker who has a needle-stick injury.
The relevant questions we need to address before beginning.
Our calculations are, what is the risk of infection after the needle-stick?
What drugs are available in order to prevent HIV?
How effective are those drugs?
What are their side effects?
And I would like to point out in this case,
we're not considering what any of the costs are.
But in a more robust model, we'd want to answer all these questions and
try to identify the cost associated with all of them.
Although we're not directly considering costs,
we are considering benefits and harms.
So if there's no treatment, there's no side effects, and
no unnecessary treatment.
However, there's still a risk of developing HIV.
If treatment is given, there's a reduced risk of HIV,
but there is also potential unnecessary treatment.
Because it's possible that the person would not have gotten HIV,
even without having received the treatment.
There's also the side effects from the treatment, and
after the treatment there's still a risk of developing HIV.
Note, this isn't no risk, it's a reduced risk.
Now that we've reviewed the types of information that's necessary to evaluate
the model, we can discuss how to evaluate the model.
To do so, we apply probabilities to each possibility in the decision model,
and the outcome measures to each end point.
At each split in the decision tree or each branch off of one of the previous events,
the probabilities have to add up to 100%.
Also note that the probabilities now come measures
should come good quality research evidence.
And if assumptions are made about any particular probability or
outcome it needs to be clear why and how those assumptions were made.
Finally, to calculate the outcomes or
the final measures used to evaluate the technology decision model.
The probabilities are multiplied by the outcome measures which
results in an expected value of the outcome measure.
For this example the average risk of HIV transmission after
exposure is approximately 0.3%.
The effectiveness of the treatment is difficult to estimate, but
there are some studies that have tried to do so.
One study found that it reduced the odds of HIV infection
by 81% which is equivalent to about a 5% chance of HIV.
Now, there was some calculation that was done to get from 81%
reduced odds ratio to a 5% chance.
It's important to know how and what types of information are reported in studies,
so that you don't blindly apply probabilities to the decision model that
aren't appropriate.
Finally, there's numerous side effects that include nausea and vomiting, fatigue,
headache, abdominal pain, diarrhea, and possibly others.
The probability of getting a side effect has a wide range of estimates,
anywhere between 50 and 75%.
So in order to estimate the model we'll assume it's around 63%.
Just as it we have to identify the probabilities for the model.
We also had to identify values of the outcomes.
We can do this also from the literature.
The existing preference scores associated with HIV infection and some side effects.
Or the quality of life measures include preference scores for
HIV which are between 0.5 and.0.75, and
preference scores for some of the side effects.
The values that we can apply to the model are shown below.
Note, whichever values we apply to the model
we still need to demonstrate where and how they came from.
So that anyone using the results of the model are able to assess the validity and
reliability of the model itself.
If we again consider the graphic representation of the model for
each possible outcome.
No treatment or yes treatment, we would assign a probability,
To each event occurring.
And note, Probabilities in each case have to add the one.
So the probability in first stag of 1 plus 2 has to equal 100%.
The same here and the same here, and the same here.
The outcome measures are then applied to
the end points where outcomes are assessed.
I've omitted the full calculations from this presentation.
However, the expected utility value of no treatment is 0.99715,
and the expected value of treatment is 0.9369.
Therefore, based on this model and
the assumptions made a person is better off with no treatment.
However, this could change if the assumptions, either the probabilities or
the outcome values, were changed.
It's important to remember that the assumptions made
in order to calculate the model impact the final results.
