Download PUBH 6541 Statistics for Health Management Decision

PUBH 6541
Statistics for Health Management Decision-Making
Spring 2013
Credits: 3
Meeting Days, Times, and Places: On-line, 1/22/13-5/13/13
Instructor:
Jean Marie Abraham, Ph.D.
Office Address: Phillips-Wangensteen Building 15-227
Office Phone:
612-625-4375
Fax:
612-624-2196
E-mail:
[email protected]
Office Hours: By appointment
Teaching Assistant: Kathryn Thesing will serve as the teaching assistant. She will respond to student
questions on a “by appointment” basis and will assist with grading. She can be reached at
[email protected].
I.
Course Description
This course will provide you with an introduction to descriptive and inferential statistical methods to
inform health care organizations’ operations and strategic planning functions.
II.
Course Prerequisites
Although this course does not have any formal prerequisites, it is important that you are comfortable
with mathematics (including basic algebra), as well as Microsoft Excel 2007 or 2010, PowerPoint 2007
or 2010, and Word 2007 or 2010. The course does not assume any prior training in statistics.
However, given that this is a graduate-level course, the material will be presented at a faster pace and
in greater depth than a comparable undergraduate course.
III. Course Goals and Objectives
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To recognize the value of data-based decision-making in health care organizations.
To identify and interpret patterns in raw data using both graphical and non-graphical methods.
To understand fundamental probability concepts and their relationship to inferential statistical
methods.
To perform inferential statistical analyses, including hypothesis testing and confidence interval
construction and to be able to interpret results in the context of prevalent issues in health care
management.
To understand the fundamentals of regression analysis and to accurately interpret model results.
To apply regression analysis tools for predictive modeling and forecasting of continuous and
discrete outcomes in health care.
To use Excel-based software to conduct analyses with population-based and organization-based
health care data.
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IV. Methods of Instruction and Work Expectations
The course is divided up into multi-week modules and is organized in such a way as to provide
additional flexibility to students who need to balance their studies with other work and life
responsibilities. Each module utilizes a variety of instructional methods, including:
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Voice-over PowerPoint presentations
Camtasia Relay video presentations
Text and handout readings
Self-study exercises and suggested answers
Excel-based practice exercises and suggested answers
Excel-based assignments
This is a rigorous, graduate-level course. You should plan to spend 8-10 hours per week, on average,
to achieve mastery of the material. The amount of time spent on this course is typically related to a
student’s prior foundation in statistics and quantitative methods. This is an asynchronous online course
with one orientation session.
V.
Course Text, Lessons, Readings, Problem Sets, Software, and Supplemental Materials.
Required Textbook: Albright, Winston and Zappe, Data Analysis and Decision Making – Fourth
Edition, South-western Cengage Learning, 2011.
Readings: Textbook and other readings will be assigned.
PowerPoint: Each module will include one or more lessons. Lesson notes corresponding to the
presentations also will be provided for reference.
Problems: Mastery of statistics and quantitative methods requires practice. Each week, selfstudy exercises, Excel applications, and suggested answers will be posted. These problems will
not be turned in for a grade.
Statistical Software: Excel 2007/2010 will be used. This course also uses the student version of
the DecisionTools® Suite that comes with the textbook. The StatTools component of the suite will
be used. Instruction for using “traditional” Excel will be integrated into modules and exercises
throughout the semester.
Supplemental Materials: Periodically, we will provide links or articles that may be of interest to
you.
VI. Communications
If you have questions regarding course content or assignments, please do not hesitate to contact
the instructor or teaching assistant. We will do our best to respond within 24 hours on weekdays
and by Monday if you contact us during the weekend.
VII. Evaluation and Grading
Criteria: The final grade for this course will be determined by performance on four assignments and
participation via online discussions. The assignments will include analyses and/or interpretation of data
to inform managerial decisions by health care organizations.
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Assignment 1 (20%)
Assignment 2 (25%)
Assignment 3 (25%)
Assignment 4 (20%)
Participation (10%)
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Assignments may be done individually or in groups of two. No exceptions will be made for
collaborations in excess of two students. Assignments are due on the date indicated. Failure to submit
an assignment without prior permission of the instructor by the due date will result in a late penalty of 5
percentage points per day. If an assignment is not submitted within one week following the original due
date, no credit will be given. Permission to turn work in late without penalty will be granted only for very
serious reasons relating to medical emergency or death in family, etc. Please plan accordingly.
Participation will be evaluated based on posting to the online discussion forum during the semester.
For most modules, an issue and question(s) will be raised by the instructor. Students will be asked to
reflect on the issue or question(s) and to provide a thoughtful response for at least two modules. The
issue or question(s) may be motivated by a recently published piece of research or an organizational
case study, and may also ask you to reflect on your own experiences as they relate to the specific
topic.
Grading Scale: An A/F letter grade will be determined based on the following:
A=93-100%
Represents outstanding achievement relative to the level necessary
to meet course requirements
A- = 90-92.99%
B+ = 87-89.00%
B = 83-86.99%
Represents achievement that is significantly above the level
necessary to meet course requirements
B- = 80-82.99%
C+ = 77-79.99%
C = 73-76.99%
Represents achievement that meets the minimum course
requirements
C- = 70-72.99%
D+ = 65-69.99%
D = 60-64.99%
F = < 59.99%
No credit. Signifies work was below level of achievement that
represents minimum threshold to obtain credit or work was not
completed and there was no agreement between instructor and
student that the student would be awarded an I.
VIII. Course Outline
Face-to-Face Session: In this face-to-face session, an overview of the course logistics as well as
an introduction to data analysis and decision-making will be presented.
Readings: Albright, Winston, Zappe (AWZ): Chapter 1
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On-line Sessions
Module 1 (1/22-2/11): Visualization of Data and Descriptive Statistics
This first module introduces key concepts in applied statistics. In the first lesson, we focus on the
language of statistics and describing data. The second lesson provides an overview of methods for
summarizing the distributions of quantitative and qualitative random variables. The third lesson
provides a data-based exercise to introduce you to StatTools, which is an Excel-based statistical
software package that will be used throughout the course for conducting empirical analyses.
Lesson 1:
PowerPoint presentation: Language of statistics and describing data
Lesson 1.1 Notes
Readings: Albright, Winston, Zappe (AWZ): 2, 3.1-3.3; 3.5-3.6
Self-study exercise 1.1
Lesson 2:
PowerPoint presentation: Measures of location and dispersion
Lesson 1.2 Notes
Self-study exercise 1.2
Example of Community Health Needs Assessment – Maine (used with self-study exercise 1.2)
Lesson 3:
Heart Failure Length of Stay data-based exercise (Introduction to StatTools)
HFdata.xlsx
Camtasia relay video presentation providing a StatTools orientation for descriptive statistics
PowerPoint presentation: Review of results for Heart Failure Length of Stay Exercise
Supplemental Materials:
Installing DecisionTools on your computer
On-line Discussion:
TBD
Assignment #1
St. James Hospital Community Needs Assessment (PDF document)
sjhcommunity.xlsx (Data file)
The assignment is due on Tuesday, 2/12/12 by 9am CT.
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Module 2 (2/12-2/18): Introduction to Probability and Probability Distributions
Lesson 1:
PowerPoint Presentation: Introduction to Probability
Lesson 2.1 Notes
Self-study exercises 2.1
Lesson 2:
PowerPoint Presentation: Probability Distributions and Applications
Lesson 2.2 Notes
Self-study exercises 2.2
Supplemental Materials: TBD
On-line Discussion:
TBD
Assignment:
No assignment for this module.
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Module 3 (2/19-3/18): Inferential Methods
The third module includes six lessons that cover foundations to inferential statistical methods. The
first lesson introduces the concepts of point estimation and interval estimation. The next three
lessons introduce hypothesis testing and discuss methods for testing hypotheses regarding a
single population parameter (e.g., a single proportion), the difference of two population parameters
(e.g., two population means), and the difference of three or more population parameters. Lesson
5 provides you with instruction on how to conduct these analyses in StatTools. Lesson 6 wraps up
the module with an introduction to analyzing the relationship between two variables, including two
discrete variables and two continuous random variables.
Lesson 1:
PowerPoint presentation: Transition to Inferential Statistics (Pt. 1) and Interval Estimation (Pt. 2)
Lesson 3.1 notes
Readings: AWZ 7, 8.1-8.5
Self-study exercise 3.1
Lesson 2:
PowerPoint presentation: Hypothesis testing for a single population parameter
Lesson 3.2 notes
Readings: AWZ 7, 8.1-8.5
Self-study exercise 3.2
Lesson 3:
PowerPoint presentation: Hypothesis testing for the difference of two population parameters
(means, proportions)
Lesson 3.3 notes
Readings: AWZ 8.7, 8.8, 9.1-9.4
Self-study exercise 3.3
Lesson 4:
PowerPoint presentation: Hypothesis testing for the difference of three or more parameters
(means, proportions)
Lesson 3.4 notes
Readings: AWZ 9.5-9.8
Self-study exercise 3.4
Lesson 5:
Hospital Average Daily Census (StatTools exercise for inferential methods)
Hospdailycensus.xlsx
Camtasia relay video presentation providing StatTools instruction for inferential methods
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Lesson 6:
PowerPoint presentation: Bivariate relationships (chi-square tests of association and correlation)
Lesson 3.6 notes
Readings: AWZ 10.3, 9.6
Self-study exercise: 3.6
Camtasia relay video presentation providing StatTools instruction for chi-square tests of
association and correlation
On-line Discussion:
TBD
Assignment #2
Patient Satisfaction and Care System Switching
This assignment is due Tuesday, 3/19/13 by 9am CT.
Module 4: (3/19-4/15): Regression Analysis
(Spring Break occurs during this module, but material will be posted to maximize flexibility).
This module provides an introduction to regression analysis. This module will cover assumptions of
ordinary least squares, model specification issues, and interpretation of regression output. We will
begin with a presentation on simple linear regression. Following this, we will cover the
fundamentals of multiple linear regression, which is commonly used for predictive modeling,
forecasting, and research applications. The final lesson will provide a data-based exercise to
increase your understanding of model estimation and interpretation.
Lesson 1:
PowerPoint presentation: Simple linear regression
Lesson 4.1 Notes
Readings: AWZ: 10.1-10.4
Self-study exercise 4.1
Lesson 2:
PowerPoint presentation: Multiple linear regression
Lesson 4.2 Notes
Readings: AWZ: 10.5-10.6; 11
Skrepnek (2005) “Regression Methods in the Empiric Analysis of Health Care Data.” Journal of
Managed Care Pharmacy. 11(3): 240-251.
Lesson 3:
Factors affecting ED Length of Visit (StatTools exercise)
EDvisit.xlsx
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Camtasia relay video presentation providing StatTools instruction for estimating multiple linear
regression models and interpretation.
Lesson 4:
PowerPoint presentation: Functional Form Issues
Lesson 4.4 Notes
Readings: AWZ 10.6
On-line Discussion:
TBD
Assignment #3:
Factors associated with Length of Stay
Assignment #3 is due on Tuesday 4/16/13 at 9am CT.
Module 5 (4/16-5/13): Regression Applications (Predictive Modeling, Risk Adjustment, and
Forecasting)
Lesson 1:
PowerPoint presentation: Binary Outcome Models
Lesson 5.1 Notes
Lesson 2:
PowerPoint presentation: Introduction to Predictive Modeling and Risk Adjustment
Lesson 5.2 Notes
Readings:
“An introduction to risk assessment and risk adjustment.” AMA Practice Management Center.
Available at: http://www.ama-assn.org/resources/doc/psa/risk-assessment.pdf
Zou, Kelly., J. O’Malley, L. Mauri. “Receiver-Operating Characteristics Analysis for Evaluating
Diagnostic Tests and Predictive Models.” Circulation, 2007, 115: 654-657.
Knutson, D., M. Mella. “Predictive Modeling: A Guide for State Medicaid Purchasers.” Center for
Health Care Strategies.” Available at:
http://www.chcs.org/usr_doc/Predictive_Modeling_Guide.pdf.
Hasan et al. “Hospital Readmission in General Medicine Patients: A Prediction Model.” JGIM,
25(3): 211-219.
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Lesson 3:
PowerPoint Presentation: Fundamentals of Forecasting
Lesson 5.3 Notes
Readings: AWZ 12
Lesson 4:
Forecasting Hospital Census (Stat Tools Exercise)
Hospitaladc.xlsx
On-line Discussion:
TBD
Assignment #4:
Predicting Re-hospitalization among Nursing Home Patients
Assignment #4 is due on Tuesday 5/13/13 at 9am CT.
VIII. Course Evaluation
Beginning in fall 2008, the SPH collects student course evaluations electronically using a software
system called CoursEval: www.sph.umn.edu/courseval. The system sends email notifications to
students when they can access and complete their course evaluations. Students who complete their
course evaluations promptly can access their final grades just as soon as the faculty member renders
the grade in SPHGrades: www.sph.umn.edu/grades. All students will have access to their final grades
through OneStop two weeks after the last day of the semester regardless of whether they completed
their course evaluation or not. Student feedback on course content and faculty teaching skills are an
important means for improving our work. Please take the time to complete a course evaluation for each
of the courses for which you are registered.
Incomplete Contracts
A grade of incomplete “I” shall be assigned at the discretion of the instructor when, due to extraordinary
circumstances (e.g., documented illness or hospitalization, death in family, etc.), the student was
prevented from completing the work of the course on time. The assignment of an “I” requires that a
contract be initiated and completed by the student before the last official day of class, and signed by
both the student and instructor. If an incomplete is deemed appropriate by the instructor, the student in
consultation with the instructor, will specify the time and manner in which the student will complete
course requirements. Extension for completion of the work will not exceed one year (or earlier if
designated by the student’s college). For more information and to initiate an incomplete contract,
students should go to SPHGrades at: www.sph.umn.edu/grades.
IX. Other Course Information and Policies
Course Withdrawal
Students should refer to the Refund and Drop/Add Deadlines for the particular term at
onestop.umn.edu for information and deadlines for withdrawing from a course. As a courtesy,
students should notify their instructor and, if applicable, advisor of their intent to withdraw.
Students wishing to withdraw from a course after the noted final deadline for a particular term must
contact the School of Public Health Student Services Center at [email protected] for further
information.
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Student Conduct, Scholastic Dishonesty and Sexual Harassment Policies
Students are responsible for knowing the University of Minnesota, Board of Regents' policy on
Student Conduct and Sexual Harassment found at www.umn.edu/regents/polindex.html.
Students are responsible for maintaining scholastic honesty in their work at all times. Students
engaged in scholastic dishonesty will be penalized, and offenses will be reported to the Office of
Student Academic Integrity (OSAI, www.osai.umn.edu).
The University’s Student Conduct Code defines scholastic dishonesty as “plagiarizing; cheating on
assignments or examinations; engaging in unauthorized collaboration on academic work; taking,
acquiring, or using test materials without faculty permission; submitting false or incomplete records
of academic achievement; acting alone or in cooperation with another to falsify records or to obtain
dishonestly grades, honors, awards, or professional endorsement; or altering, forging, or misusing
a University academic record; or fabricating or falsifying of data, research procedures, or data
analysis.”
Plagiarism is an important element of this policy. It is defined as the presentation of another's
writing or ideas as your own. Serious, intentional plagiarism will result in a grade of "F" or "N" for
the entire course. For more information on this policy and for a helpful discussion of preventing
plagiarism, please consult University policies and procedures regarding academic integrity:
http://writing.umn.edu/tww/plagiarism/.
Students are urged to be careful that they properly attribute and cite others' work in their own
writing. For guidelines for correctly citing sources, go to http://tutorial.lib.umn.edu/ and click on
“Citing Sources”.
In addition, original work is expected in this course. It is unacceptable to hand in assignments for
this course for which you receive credit in another course unless by prior agreement with the
instructor. Building on a line of work begun in another course or leading to a thesis, dissertation, or
final project is acceptable.
If you have any questions, consult the instructors.
Disability Statement
It is University policy to provide, on a flexible and individualized basis, reasonable accommodations
to students who have a documented disability (e.g., physical, learning, psychiatric, vision, hearing,
or systemic) that may affect their ability to participate in course activities or to meet course
requirements. Students with disabilities are encouraged to contact Disability Services to have a
confidential discussion of their individual needs for accommodations. Disability Services is located
in Suite180 McNamara Alumni Center, 200 Oak Street. Staff can be reached by calling 612/6261333 (voice or TTY).
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ADDENDUM
PubH 6541 (Statistics for Health Management Decision-Making) NCHL*
Competencies
Based on the course objectives listed in the Self-Study Year syllabus, the following
competencies have been addressed by this course:
3 – Analytical Thinking
12 – Information Technology Management
14 – Innovative Thinking
17 – Performance Measurement
24 – Strategic Orientation
The course objectives are listed here with the corresponding NCHL competencies:
To recognize the value of data‐based decision‐making in health care organizations. L3.4; L12.1; L17.2 To identify and interpret patterns in raw data using both graphical and non‐graphical methods. L3.4; L14.2 To understand fundamental probability concepts and their relationship to L3.4; L17.2 inferential statistical methods. To perform inferential statistical analyses, including hypothesis testing and confidence interval construction and to be able to interpret results in the context of prevalent issues in health care management. L3.4; L17.2; L24.1 To understand the fundamentals of regression analysis and to accurately interpret model results. L3.4; L17.2 To apply regression analysis tools for predictive modeling and forecasting of continuous and discrete outcomes in health care. L3.4; L17.2 To use Excel‐based software to conduct analyses with population‐based and organization‐based health care data. L3.4; L17.2 *The MHA program uses the National Center for Healthcare Leadership (NCHL) Health Leadership
Competency Model (v 2.1). Copyright 2006. NCHL. All rights reserved.
The number following the decimal indicates the level to which that competency is addressed, as further
described in the Competency Model, available here:
http://www.nchl.org/Documents/NavLink/NCHL_Competency_Model-full_uid892012226572.pdf.
Addendum Page 1 of 1