Prepared for Richard P. | Delivered August 12, 2019
Predictive analytics in HealthCare
To find cases studies of how predictive analytics improve outcomes in healthcare, especially in target intervention and improvement of safety for patients. This information will be used to prepare a client pitch.
CASE STUDY 1: UNION GENERAL
In Northern Georgia, Union General, a modest non-profit healthcare provider, struggled with their handling of
20 TB o
f data related to patients. This data included digital p
atient charts and electronic
Due to the poor quality of the data stored and the system, hospital staff had to s
pend time manually u
sing Excel files, which had an impact on the quality of service.
The introduction of a
new advanced analytics
system allowed case management workers at Union General to perform a monthly review and analysis of readmissions. This information is used to develop
in order to lower rates of readmissions and keep patients away from hospitals.
Previous to the implementation of the new predictive analytics system, case managers could only manage to perform their
It would also allow case management staff to
s in real-time rather than retrospectively on the old system.
CASE STUDY 2: JOHN HOPKINS
John Hopkins Hospital has developed a
predictive model in oncology
to assist in the customization of chemotherapy treatment plans for people with cancer.
The help of this technology would allow doctors to minimize the body areas that will be subjected to radiation
Performing this task manually is a
and time-consuming process that is made quicker and simpler by the introduction of predictive analytics.
The algorithm developed by the team at John Hopkins allows predicting with precision the
amount of radiation
that will be taken by a vital organ, basing itself on its spatial relationship with the affected cancerous tissue.
The aim is to limit the
by radiation to vital organs surrounding the infected tissue.
CASE STUDY 3: PENN HEALTH
Penn Health is developing predictive analytics for the
diagnosis of lethal diseases
before they occur.
The project consists in
creating learning models
from historical data.
The first type of data used was digital health records as well as laboratory information, and the first models were created to predict
heart failure and severe sepsis.
Penn Health is also planning on
exporting these models
to other institutions.
For the diagnosis of severe sepsis, Penn Health used six vital measurements, and its predictive model used more than
This has been a successful effort, allowing the medical institution to detect
80% of cases
within 30 hours of the manifestation of the symptoms.
Its hearth failure algorithm has allowed detec
more patients that were at risk of cardiac arrest.
In addition to this public search, we scanned our proprietary research database of over 1 million sources and were unable to find any specific research reports that address your goals.
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