Welcome to Data Science Methodology 101 From Deployment to Feedback - Deployment!
While a data science model will provide an answer, the key to making the answer relevant
and useful to address the initial question, involves getting the stakeholders familiar
with the tool produced.
In a business scenario, stakeholders have different specialties that will help make
this happen, such as the solution owner, marketing, application developers, and IT administration.
Once the model is evaluated and the data scientist is confident it will work, it is deployed
and put to the ultimate test.
Depending on the purpose of the model, it may be rolled out to a limited group of users
or in a test environment, to build up confidence in applying the outcome for use across the board.
So now, let's look at the case study related to applying Deployment"
In preparation for solution deployment, the next step was to assimilate the knowledge
for the business group who would be designing and managing the intervention program to reduce
readmission risk.
In this scenario, the business people translated the model results so that the clinical staff
could understand how to identify high-risk patients and design suitable intervention
actions.
The goal, of course, was to reduce the likelihood that these patients would be readmitted within
30 days after discharge.
During the business requirements stage, the Intervention Program Director and her team
had wanted an application that would provide automated, near real-time risk assessments
of congestive heart failure.
It also had to be easy for clinical staff to use, and preferably through browser-based
application on a tablet, that each staff member could carry around.
This patient data was generated throughout the hospital stay.
It would be automatically prepared in a format needed by the model and each patient would
be scored near the time of discharge.
Clinicians would then have the most up-to-date risk assessment for each patient, helping
them to select which patients to target for intervention after discharge.
As part of solution deployment, the Intervention team would develop and deliver training for
the clinical staff.
Also, processes for tracking and monitoring patients receiving the intervention would
have to be developed in collaboration with IT developers and database administrators,
so that the results could go through the feedback stage and the model could be refined over
time.
This map is an example of a solution deployed through a Cognos application.
In this case, the case study was hospitalization risk for patients with juvenile diabetes.
Like the congestive heart failure use case, this one used decision tree classification
to create a risk model that would serve as the foundation for this application.
The map gives an overview of hospitalization risk nationwide, with an interactive analysis
of predicted risk by a variety of patient conditions and other characteristics.
This slide shows an interactive summary report of risk by patient population within a given
node of the model, so that clinicians could understand the combination of conditions for
this subgroup of patients.
And this report gives a detailed summary on an individual patient, including the patient's
predicted risk and details about the clinical history, giving a concise summary for the
doctor.
This ends the Deployment section of this course.
Thanks for watching!
