Understaffing, available time, deep-dive and research into available but scattered knowledge source from other experts. All common problems for professional of today. The solution, use existing recommendations and create decision-trees to simplify the next steps to take.
Asking the right questions
Asking the right questions is crucial in any topic, especially in the field of medicine and leads to effective results. The wrong questions lead to ineffective results. A few simple steps to consider for asking the right questions, keep questions:
- Simple & Direct Reduce the amount of possible answers to "I don't know" and keep it as much to boolean-like answers such as "Yes" or "No" to reduce errors in the communication of the question you're about to ask your audience.
- Specific & Relevant If applying a more "open" question, e.g. "Are there any treatments or medicines that occurred within X months?" you can provide answers that are more dedicated to a certain outcome.
- Tactical & Technical Match the group of expertise, e.g. humans or homo sapiens in simple terms mean the same thing, but homo sapiens mean so much more in regards to evolution and historic: biology, psychology and culture for example, by default much more specific by just simply using technical language.
Our many years of experience of in-house consultants and marketers know how to ask the right questions and country-specific market regulations. If you need help building your decision tree or customer journey in general. Let's get in touch, the first two hours on us!
How decision trees can be applied
Healthcare professionals to make the best possible decisions
The field of medicine has been rapidly evolving and advances in technology have helped immensely in aiding physicians to make the best possible decisions when working with patients. One way in which technology has been influencing the medical field is the use of prediction and classification. Optimization strategies, such as limiting the number of false positives, have increased the validity of algorithms.
Thousands of professionals are using these decision trees to make smart and quick decisions they might otherwise not think about in important situations, we're all humans after all.
How Amsterdam UMC is using decision trees across the country to optimize the field of medicine by providing researched guidelines as simple to use decision trees and by asking the right questions.
It is important to note that usually decision trees are built upon statistical facts, recommendations and guidelines made by other experts about the topic in question. Always make use of your own expertise and thinking, and recommend others to do the same.
Multiple sources of data into direct automation
From existing data through prepared and manually defined decision trees into automated triggers to release the next action in line. The Pharma360 platform has it's own API and integrations with many marketing and software automation platforms to automate the next actions that need to be taken to improve the customer journey and increase customer lifetime value.
For example, from an invite to follow a specific campaign or course, through the most simple authentication process into full accreditation of the healthcare professional, all automated using decision trees of the Pharma360 platform.
Can Machine Learning and Artificial Intelligence play a role in decision trees?
Definitely! Artificial Intelligence and Machine Learning play a huge roles in decision trees, adopting these technologies and integrating them within decision trees allow for more accuracy.
Learning from our own end-user's decisions, scientific data and experts in the field to train the data for accurate results makes it possible to improve any decision making process on Earth.
How can we ensure that decision trees are validated?
Every single decision tree is validated by continuously scanning through dataset of questions, answers, advice, tools and more. In particular we are looking for conflicting statements, duplicated questions and answers, dead (open) ends and much more.
There are warning messages in the decision tree configurator that show the experts what might be going wrong in real time and they can act accordingly to possibly adjust necessary changes that will make the decision trees even better.
How decision trees work and what possibilities are there?
Decision trees are one such tool that is applied to help determine the best course of the next condition or outcome. These, as well as other, strategies can be implemented in decision support systems that are designed to make sense of the large amounts of data that are constantly being gathered.
A decision tree is typically diagrammed as a branching structure that starts at the top with the condition, where each line represents a different condition, in alignment and progress towards the best possible outcome.
You can make a decision here and go left, right, start, end, another decision tree, use a scoring system, calculators and much more. Each and every of these possible decisions would represent a next outcome. The lowest level on the tree usually presents the most likely result.
For example, if we were looking at a pseudo decision tree, the diagram might look like this:
This is a pseudo example of a decision tree. For real examples on how decision trees could work for you or your organization, please contact us at smit.net/contact.
It is important to note that usually decision trees are built upon statistical facts, recommendations and guidelines made by other experts about the topic in question. Also, always make use of your own expertise and thinking, and recommend others to do the same.