Healthcare

Forecasting numbers of patients as reference for medical profession enrollment

Application Area: 

Project Details

Term: 

Fall 2016

Students: 

Yu-Chu Shih, Tzu-Han Hung, Yi-Chun Chuang, Tonny Meng-Lun Kuo

University: 

NTHU

Presentation: 

Report: 

Every year, Ministry of Health and Welfare in Taiwan (MHW) decides the capacity of medical students to ensure each division includes enough doctors in the future. However, a recent report indicates the huge shortage of doctors in five demanding divisions which leads to serious problems in hospital management. Deciding the capacity of medical students in specific divisions is challenging for MHW.

Forecasting Top 3 Cancer Rate in Each Gender for New Insurance Product Design

Application Area: 

Project Details

Term: 

Fall 2016

Students: 

Kuan-Yu Chen, I-Chun Chao, Edward Song, Yi-Wei Lai, Yen-Ju Tseng

University: 

NTHU

Presentation: 

Report: 

There are some reasons to work on this topic. Medicare doesn’t compensate that much because of the strict compensation policy. Due to the policy constraint, medical report for compensation is required. The size of tumor cannot really show the severity which is contrary to the policies nowadays. And the most expensive treatment, target therapy content, will not be paid. That’s why the cancer insurance products come out – try to complement the gap that Medicate doesn’t cover.

Optimum Staff Schedule for a German Drug Company by Forecasting Customer Footfalls

Application Area: 

Project Details

Term: 

2017

Students: 

Ashish, Saurabh Shekhar, Rishab Choraria, Kirti Pandey, Rahul Singh, Krishna Guha

University: 

ISB

Presentation: 

Report: 

a. Problem Description
Our client is a German drug manufacturer who owns and operates six stores in different parts of
Germany. Each store has a unique layout and some stores are open for fewer days than others. Moreover, customer footfall varies by location. Our client contracts staffing personnel on a daily basis from a staffing agency for all the stores, and each contracted staff is paid on per hour basis every day. The
management in our client’s organization has been tasked with an objective to bring down the staffing

Forecasting Suicides in US for Allocating Counsellors

Application Area: 

Project Details

Term: 

2017

Students: 

Aniket Singh, Mithun Mohandas, Rahul Agrawal, Saurav Basu, Vijay Swaminathan

University: 

ISB

Presentation: 

Report: 

This report focusses on creating monthly forecasts of suicides using firearms for the year 2015.
Action Alliance is a big organization with operations in various social areas. With such large
requirements of contractual work force, there is huge scope of cost savings by efficient human
resource allocation.
This forecasting exercise predicts with 95% accuracy, monthly suicides involving firearms. The
model also predicts the deaths by gender, age and location of death. With a year’s view of the

Preventing exacerbation in respiratory patients by early prediction

Application Area: 

Project Details

Term: 

Fall 2014

Students: 

Kun-Lin Tsai, Yuan-Yu Zhang, Chris Yen Chen Lo

University: 

NTHU

Presentation: 

Report: 

Customers: Public health care system (government public welfare department)
The expense on those patients is more than 3 million dollars per year (half patients compared to Diabetes, but double expense).

Optimizing Operation Room Utilization by Predicting Surgery Duration

Application Area: 

Project Details

Term: 

Fall 2014

Students: 

Chou-Chun Wu, Li-Chan Chen, Dai-Sin Li

University: 

NTHU

Presentation: 

Report: 

Business Background and Motivation: Our main client is the newly elected director of the hospital who found that they are losing money in the operating room department due to the improper management on the surgery scheduling. Thus, he arranged a special committee to solve this hard task. The business problem we face is that for surgery scheduling it is often subjectively assigned by the operating doctors. For the doctors, each of them has different preference of time schedule and even the operation rooms which results in the difficulty of proper scheduling.

Predicting the outcome of malpractice lawsuits

Application Area: 

Project Details

Term: 

2005

Students: 

Andrea Chod, David Hofberg, John Jepsen, Christine Nguyen, Eric Zirofsky

University: 

UMD

Presentation: 

Judgment or settlement is an important consideration for professional reputation of doctors in malpractice lawsuits.

The project aim is to build a model for predicting the outcome of a malpractice suit: whether it will result in a settlement or judgment, using data from the National Practitioner Data Bank

Understanding Health Insurance Coverage in the US

Application Area: 

Project Details

Term: 

2007

Students: 

Jason Davis, Clarette Kim, Hari Kosaraju, Scott Wood

University: 

UMD

Presentation: 

Report: 

Our goal was to use explanatory data mining techniques to better understand the characteristics of the U.S. population that lack health insurance. Our results will be used to inform health policy and business officials as they develop strategies to address the uninsured population.

Predicting Delays in the Operating Room

Application Area: 

Project Details

Term: 

2007

Students: 

Chris Low, Igor Nakshin, Xinyang Zou

University: 

UMD

Presentation: 

Report: 

According to Press Ganey’s Physician’s Office and Outpatient Pulse 2007, a widely used report
in health care industry, on time appointment performance in ambulatory surgery is one of the
most important factors with respect to patient satisfaction. However, how to best optimize the
O.R. scheduling has been a long standing, highly debated issue within the healthcare industry.

Quality-of-care factors that differentiate hospitals in the Northeast vs. other regions

Application Area: 

Project Details

Term: 

2008

Students: 

Kenyon Crowley, Selim Geron, Shih-Heng Hung, Seth Kelly, Liz Slobasky

University: 

UMD

Presentation: 

Report: 

The goal of this analysis is to look at the characteristics of U.S. hospitals to see if hospitals in certain geographical regions shared common features, particularly in the Northeast versus other areas of the country. The data used were taken from the HIMSS Analytics Database, which comprises data from over 5,000 hospitals. Specifically, we focused on variables relating to finances, quality of care and service metrics, and types of technology used by the facility.

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