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Revenue Cycle Analytics

//Revenue Cycle Analytics
Revenue Cycle Analytics 2018-04-26T20:32:57+00:00

Our Expertise

Amstat Analytics Group has become nationally recognized for helping hospitals chart their new course with greater efficiency and agility. From implementation to data migration to tuning and optimization to advanced analytics, the Amstat Analytics Group Professional Services team will work with you every step of the way. Our clients cite these reasons for choosing to work with us:

  • All of our principals have doctorates at leading universities including Harvard, Stanford, and Columbia.
  • Amstat Analytics Group has numerous healthcare associates across multiple locations with proven domain competence.
  • The team includes doctors, clinical specialists, statisticians, and data scientists.
  • We have extensive backgrounds in healthcare analytics and over 100 years of practical experience in the healthcare field.
  • We have more than 650 skilled resources dedicated to healthcare research, reporting, and analytics practice.
  • We bring our cumulative experience working with close to 900 hospitals on revenue cycle issues. You benefit from your peers’ successes solving the same problems you face today.
  • Our consultants work closely with your staff so they have the skills and tools to keep improving performance long after we’re gone.
  • Our recommendations are based on more than 100 years of best practice research on hospital management, including intensive research into techniques to optimize patient access processes.
  • We can help providers operate their revenue cycle optimally.
  • We are able to develop, educate, and implement the best solution for your facility or medical practice.
  • We work with management to develop a strategy and a team to implement “best practices” and maintain optimization.

Doctorates at Leading Universities Including Harvard, Stanford, & Columbia

Doctorates at Leading Universities Including Harvard, Stanford, & Columbia

Extensive Backgrounds in Healthcare Analytics

Extensive Backgrounds in Healthcare Analytics

Numerous Healthcare Associates

Numerous Healthcare Associates

Image result for predictive analytics for healthcare, image

The problem: Hospitals are experiencing a number of problems. Those include lower reimbursement resulting in margin compression, disparate data systems that result in inconsistent, expensive data and metrics, and data that are updated weekly, monthly or quarterly, which results in a lack of any actionable workflow.

Solution: Among the most effective emerging tools for healthcare providers to streamline and optimize processes while maximizing revenue is predictive analytics. A majority (93%) of healthcare payers and providers believe predictive analytics is important for the future of their business, according to a 2017 Predictive Analytics in Healthcare Trend Forecast by the Society of Actuaries. Hospital revenue cycle management experts promote medical predictive analytics as the newest solution to translating massive loads of health data into actionable insight to improve financial performance.

Predictive analytics has been successfully deployed across nearly every industry and sector of the US economy. The ability to identify the likelihood of future outcomes based on historical data is around us every day—think Amazon and Netflix recommendations—but have you ever wondered what it can do for your organization?

Every hospital has amassed large data assets that characterize their interactions with patients, physicians, and payers, among others. We’ll zero in on the process and work needed so that a hospital can tap into those data assets to operate more efficiently from a revenue cycle perspective.

Accomplishing this requires connecting data assets with technology – specifically predictive analytics, data warehousing and reporting. Implementing predictive analytics can enable a healthcare organization to improve processes and payment collection opportunities, create consistent and measurable metrics across the revenue cycle, and manage staff resources in a way that streamlines workflow and reduces unessential tasks.

We apply this data in ways that improve financial performance, similar to how other industries have been doing for decades. We can help leaders make smarter decisions. We can:

  • Implement predictive analytics to enable you to improve revenue cycle processes and payment collection opportunities, create consistent and measurable metrics across the revenue cycle, and manage staff resources in a way that streamlines workflow and reduces unessential tasks
  • Use predictive models to figure out if a charge is missing; a patient will pay his or her part of the bill; a patient will be readmitted, or a diagnosis-related group code was assigned in error
  • Leverage predictive analytics to look back at a case or issue and see how it was handled
  • Use predictive analytics to achieve greater revenue cycle efficiency and better overall resource allocation
  • Predict issues that may come up in the future
  • Use algorithms to examine historical data and make correlations
  • Predict not only if a patient is likely to pay, but also how much he or she is likely to pay
  • Create self-pay predictive models that include a number of criteria, such as self-pay type, patient class (whether outpatient or inpatient), payment history, and debt history
  • Analyze the company’s payment habits, provide the probability that the company will pay a trade account in a “severely delinquent” manner (90+ days beyond terms) within the next 12 months through the Dynamic Delinquency Score (DDS), and include comparisons to other companies within the same industry
  • Answer even more
    • Is there a deterioration in payment habits?
    • Will they pay me on time?
    • How much credit do suppliers usually extend to them?
  • Explore the hidden opportunity in hospital charging data—missing charges, undercoding, and meaningful coding anomalies
  • Use predictive analytics and data mining to uncover these hidden pockets of opportunity
  • Analyze the real-world impact on revenue integrity and charge-capture KPIs
  • Leverage predictive analytics to deliver improved financial performance
  • Use predictive analytics for audits to identify missing revenue
    • Review the charges on the claim with an application that can predict errors
    • Help you source the root cause of errors
    • Identify drivers behind patient readmission
  • Identify the normal revenue-cycle processes, model them, and then pinpoint the cases that exhibit anomalies
  • Use algorithms to examine historical data and make correlations
  • Identify trends by drilling down to the staff, department, and service levels to uncover insightful details
  • Help you gain insight into how to more effectively invest time and efforts

Result: The result should be a system that focuses the right staff on the right accounts at the right time to maximize return and efficiency. Ultimately it should allow the revenue cycle staff to work smarter.

Applying predictive analytics to your business processes will ultimately yield both quantitative and qualitative results. The quantitative results are easily measured by increased point-of-service collections, a reduction in bad debt and the cost to collect, all while maintaining or lowering operating expenses. The qualitative results come in the form of employee and patient satisfaction.

Information empowers employees to work smarter, not harder. Many times front-line employees are asked to use a one-size-fits-all approach to processing patient accounts because they have no insight into the patient’s ability to pay. Consumer credit data can provide employees with a clear indication of a patient’s capacity to pay, therefore allowing the accounts to be processed in a more efficient manner.

Moreover, predictive analytics allows hospital employees to prioritize actions with respect to open account balances by helping to identify the accounts with the highest likelihood of collections. The result is an increase in productivity, an improvement in financial results and satisfied employees.

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The problem: The challenge of bundled payments and re-admission rates is increasing billing complexity and denials. Underpayment has become so commonplace it is assumed. Denial patterns have become increasingly impenetrable. ICD-10 is poised to usher in an entirely new class of billing mistakes. In this environment, you are being judged by your ability to recover revenue and accelerate cash flow.

As a revenue cycle leader, you want to gauge the denial risk of an account before the claim leaves your organization. You want denial issues and trends identified by payer, at the plan and service level to give pointed direction as to where the originating denial issues lie. You want to know about the hidden denial patterns that are attributing to your net revenue leakage. You want your revenue analyst team working in just that capacity, and getting away from simply preparing reports. Finally, you want to view commonalities among your peers with respect to denial patterns and see where your organization stands out from the rest of the pack.

Solution: Amstat Analytics Group will give you the strategic lens that is sorely needed when it comes to denials avoidance. We will provide your team with specific areas of revenue-risk to focus efforts to correct the originating issue(s). We complement your current denials management and workflow systems as we examine initial denial issues based on predictive modeling. Predictive modeling can be used to estimate the likelihood of a claim denial prior to submission. This allows staff to focus only on those claims likely to be denied – based on past denial patterns – in order to minimize delays in reimbursement.

We will provide you with a comparative analysis of initial denial rate at the peer group and payer level.

We can:

  • Leverage predictive analytics to predict where denials are likely to occur next and drive the operational change needed to reduce denial rates. This allows staff to focus only on “those claims likely to be denied – based on past denial patterns – in order to minimize delays in reimbursement.
  • Understand what non-obvious factors are correlated and contributing to your initial denial rate
  • Get denial overturn rates with associated net revenue
  • Help you redesign your front office to maximize point-of-service collections, optimize financial counseling, and improve customer service
  • Help you optimize your registration processes to simplify the medical necessity and authorization and reduce the likelihood of downstream denials
  • Benchmark on a payer-to-payer and peer-to-peer basis
  • Aggregate disparate data streams into one analytic application that delivers the revenue cycle decision support needed in today’s climate
  • Audit your final denial rate
  • Store your claim and remittance data within one application
  • Address clinical and operational back office issues that are causing payment denials and delays
  • Provide comparative analysis and benchmarking that scores payer performance based on claim, rejections, denials, and exceptions

The problem: In today’s evolving healthcare climate, hospitals and health systems are challenged to provide higher-quality care while operating on leaner budgets. Namely, the shift away from fee-for-service to fee-for-value aims to make higher-quality, lower-cost care more accessible to patients. New reimbursement models, in addition to growing consumerism and competition, further emphasize the need to recover and maximize every dollar.

Solution: Big data and applied analytics can help organizations address these challenges, offering insights into the revenue cycle that otherwise would be difficult to attain at such a granular level. Hospitals and health systems that use strong data analytics service have the potential to measure, compare, and learn from nearly every element in the revenue cycle.

We can:

  • Help you make better decisions by leveraging data and analytics in revenue cycle management
  • Provide you with specific areas of revenue-risk to focus efforts to correct the originating issue(s)
  • Provide data and analytic support for administrators regarding decision making about program and service performance
  • Improve your workflows, operational performance, and financial results by leveraging your data across the revenue cycle, matching it, and analyzing the account across the various revenue cycle workflows and transactions
  • Ensure accurate reimbursement by analyzing workflows and optimizing activities
  • Create and monitor revenue cycle KPIs around pre-service, point-of-service, post service, and denials to provide data points needed for process and financial optimization
  • Maximize return on investment
  • Help staff aware of the issues that truly require investigation, action, and resolution
  • Enable the calculations of HFMA Map Keys and NAHAM Access keys for true peer-to-peer benchmarking
  • Help you build a system that focuses the right staff on the right accounts at the right time to maximize return and efficiency
  • Help you reduce manual tasks, and increase productivity
  • Compare revenue and collection performance to your peers
  • Help providers achieve an optimized revenue cycle process through an integrated approach, rather than a series of unconnected steps

Image result for healthcare, image

The problem: It is a common theme in our discussions with CFOs and CEOs across the country: healthcare is moving from fee-for-service, volume-based reimbursement to quality-based reimbursement. The days of higher volumes equating to more revenue will soon be fully in our rear-view mirror. This change may already be having a huge impact on your reimbursement. Healthcare executives across the country often ask us: How can I ensure that quality-based reimbursement does not send my hospital’s finances down the drain?

Solution: expand the role of clinical documentation improvement (CDI) to review records for documentation that impacts quality measures. By doing this, you will make sure that you get paid the right amount for the high-quality care your hospital provides and not get dinged for quality scores that do not reflect reality. We can:

  • Help you get the most out of your CDI program
  • Help your staff take care of your CDI program effectively, optimize processes, and strengthen interdepartmental relationships—whether you already have a clinical documentation improvement program in place or not
  • Analyze clinical and financial data and review charts to uncover your biggest opportunities to boot revenue and enhance quality scores
  • Analyze which physicians have the most opportunity to improve their documentation
  • Assess your preparedness for ICD-10 and create a customized implementation blueprint

Dr. Raj Singhal, MD., Director, Pediatric Anesthesiology, Phoenix Children’s Hospital

“Dr. Ann has been instrumental in helping with our statistical needs. In addition to her professionalism, she has been prompt and thorough with all of our requests. Dr. Ann’s work is impeccable, and I would recommend her services to anyone in need of assistance with statistical methods or interpretation. We plan on using Dr. Ann for all of our future needs, and I am thrilled to have been introduced to her.”

Dr. Raj Singhal, MD., Director, Pediatric Anesthesiology

Dr. Haritha Boppana, MD, DHA, GHS Greenville Memorial Hospital 

“I am a physician and was in need of statistical analysis of research data. I found them on online search. Dr. Ann called me and explained the process involved in data analysis. Dr. Ann was always very prompt, helpful, intelligent and took time explaining the various tests used in conducting data analysis. Thank you so much!! I look forward to working with you in the future.”

Dr. Haritha Boppana, MD, DHA

Dr. Vincent Salyers, Dean, Faculty of Nursing, MacEwan University

“I have worked closely with them on the data analysis/results of two research projects so feel as though I am knowledgeable about their expertise. On all accounts, the company provided me with reliable statistical analysis and results that I could translate into publishable format. They are conscientious experts who provide keen insights into appropriate statistical analysis given various data sets. I highly recommend them for your statistical support needs!”

Dr. Vincent Salyers, Dean, Faculty of Nursing

Dr. Zamir S. Brelvi MD, PhD., CEO & Co-Founder, EndoLogic

“We have been very pleased with working with them. The service was custom tailored and on time completion. The statistical report was detailed with excellent graphics. The cost of the services was affordable for a start-up company such as EndoLogic! Dr. Ann is very detail oriented and likes to know the project thoroughly that is being analyzed.”

Dr. Zamir S. Brelvi MD, PhD., CEO & Co-Founder

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“My project required the analysis of a complex survey that required a great deal of help in organizing the data and analyses. In addition, the project required a quick turn-around. They asked all the right questions, made realistic and helpful suggestions, and completed the project in a timely manner. They were professional and helpful throughout the process. I highly recommend them.”

Dr. Nancy Allen, Ph.D., Curriculum and Technology Consultant

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Dr. David Fetterman
Dr. David FettermanAdvisory Board (Fetterman & Associates, President)
EDUCATION

Ph.D., Stanford University
Master’s Degree, Stanford University
Master’s Degree, Stanford University

EXPERIENCE

Stanford University, Professor
School of Medicine, Stanford University, Director of Evaluation

HONORS (selected)

American Educational Research Association Research on Evaluation Distinguished Scholar Award, 2013
American Evaluation Association Advocacy and Use Evaluation Award, 2014
Lazarsfield Award for Contributions to Evaluation Theory, American Evaluation Association, 2000
Mensa Education and Research Foundation Award for Excellence, 1990.
Myrdal Award for Cumulative Contributions to Evaluation Practice, American Evaluation Association, 1995
Outstanding Higher Education Professional, Neag School of Education, University of Connecticut, 2008
Who’s Who in America, 1990, 1995-1996, 1999, 2008-2012
Who’s Who in American Education, 1989 90, 1995-96, 2003
Who’s Who in Science and Engineering, 2010, 2011
Who’s Who in the World, 2011, 2012, 2013

PROJECTS (selected)

$15 Million Digital Divide Project, Hewlett-Packard Philanthropy and Education
W. K. Kellogg Foundation
Tobacco Prevention, Minority Initiative Sub-recipient Grant Office, University of Arkansas at Pine Bluff
Tsholofelo Community, South Africa
Corte Madera, Portola Valley School District, CA
Family and Children Services, Palo Alto, CA
Ministry of Health and Jimma University, Ethiopia
BUILD, Palo Alto
Case Method, Columbia School of Journalism
Digital Media Center, Knight Foundation
Knight New Media Center, Knight Foundation
Te Puni Kokiri, Ministry of Maori Development, New Zealand
National Institute of Multimedia Education, Japan
Knight Foundations, Western Knight Center for Specialized Journalism
Mosaic’s Project, California State University
Arkansas Department of Education
One East Palo Alto. City Revitalization Project Hewlett Foundation
National Indian Child Welfare Association. Intertribal Council of Michigan, Hannahville Indian Community
Independent Development Trust, Cape Town, South Africa
California Arts Council

BOOKS (selected)

Fetterman, D.M. (2013). Empowerment Evaluation in the Digital Villages: Hewlett-Packard’s $15 Million Race Toward Social Justice. Stanford: Stanford University Press. (See Stanford Social Innovations site: http://www.ssireview.org/articles/entry/empowerment_evaluation_in_the_digital_villages_hewlett_packards_15_million)
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2014) (eds.) Empowerment Evaluation: Knowledge and Tools for Self-assessment, Evaluation Capacity Building, and Accountability. Thousand Oaks, CA: Sage.
Fetterman, D.M., Rodriguez-Campos, L., and Zukowski, A. (in press). Collaborative, Participatory, and Empowerment Evaluation: Stakeholder Involvement Approaches to Evaluation. New York: Guilford Publications.
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2015). Empowerment Evaluation: Knowledge and Tools for Self-assessment, Evaluation Capacity Building, and Accountability. Thousand Oaks, CA: Sage.
Fetterman, D.M. and Wandersman, A. (2005). Empowerment Evaluation Principles in Practice. New York: Guilford Publications. (Preview.)
Fetterman, D.M. (2001). Foundations of Empowerment Evaluation. Thousand Oaks, CA: Sage. (Preview.)
Fetterman, D.M., Kaftarian, S., Wandersman, A. (Eds.) (1996). Empowerment Evaluation: Knowledge and Tools for Self-assessment and Accountability. Newbury Park, CA: Sage.
(Preview.)
Fetterman, D.M. (Ed.) (1993). Speaking the Language of Power: Communication, Collaboration, and Advocacy. London, England: Falmer Press. (Preview.)

CHAPTERS AND ARTICLES (selected – over 100)

Fetterman, D.M. (in press). Empowerment Evaluation. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
Fetterman, D.M. and Ravitz, J. (in press). Evaluation Capacity Building. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. Thousand Oaks, CA: Sage.
Fetterman, D.M. (in press). Empowerment Evaluation: Linking Theories, Principles, and Concepts to Practical Steps. In Secolsky, C. and Denison, D.B. (eds.) Handbook on Measurement, Assessment, and Evaluation in Higher Education (2nd edition). New York: Routledge.
Fetterman, D.M. (2015). Empowerment Evaluation. International Encyclopedia of the Social and Behavioral Sciences, 2nd edition.
Mansh, M., White, W., Gee-Tong, L., Lunn, M., Obedin-Maliver, J., Stewart, L., Goldsmith, E., Brenman, S., Tran, E., Wells, M., Fetterman, D.M., Garcia, G. (2015). Sexual and Gender Minority Identity Disclosure During Undergraduate Medical Education: “In the Closet” in Medical School. Academic Medicine, 90(5):634-644.
Wang JY, Lin H, Lewis PY, Fetterman DM, Gesundheit N. (2015). Is a career in medicine the right choice? The impact of a physician shadowing program on undergraduate premedical students. Acad Med. May, 90(5):629-33. doi: 10.1097/ACM.0000000000000615
White, W., Brenman, S., Paradis, E., Goldsmith, E.S., Lunn, M.R., Obedin-Maliver, J., Stewart, L., Tran, E., Wells, M., Chamberlain, L.J., Fetterman, D.M., and Garcia, G. (2015). Lesbian, Gay, Bisexual, and Transgender Patient Care: Medical Students’ Preparedness and Comfort. Teaching and Learning in Medicine: An International Journal. Volume, 27, Issue 3: 254-263
Obedin-Maliver, J., Goldsmith, E.S., Stewart, L., White, W., Tran, E., Brenman, S., Wells, M., Fetterman, D.M., Garcia, G., Lunn, M.R. (2011). Lesbian, Gay, Bisexual, and Transgender-Related Content in Undergraduate Medical Education. JAMA, 306(9):971-977.
Fetterman, D.M., Kaftarian, S., and Wandersman, A. (2015). Empowerment evaluation is a systematic way of thinking: A response to Michael Patton Empowerment evaluation: Knowledge and tools for self-assessment, evaluation capacity building, and accountability. Evaluation and Program Planning 52 (2015) 10–14
Fetterman, D.M. (2011). Empowerment Evaluation and Accreditation Case Examples: California Institute of Integral Studies and Stanford University. In Secolsky, C. and Denison, D.B. (eds.) Handbook on Measurement, Assessment, and Evaluation in Higher Education. New York: Routledge.
Fetterman, D.M., Deitz, J., and Gesundheit, N. (2010). Empowerment evaluation: A collaborative approach to evaluating and transforming a medical school curriculum. Academic Medicine, 85(5):813-820.
Fetterman, D.M. (2009). Empowerment evaluation at the Stanford University School of Medicine: Using a Critical Friend to Improve the Clerkship Experience. Ensaio: Avaliação e Políticas Públicas em Educação. Rio je Janeiro, 17(63):197-204.
Fetterman, D.M. (2004). Empowerment Evaluation’s Technological Tools of the Trade. Harvard Family Research Project. The Evaluation Exchange, X 3, p. 8-9.
Fetterman, D.M. (2003). Empowerment Evaluation Strikes a Responsive Chord. In S. Donaldson & Scriven, M. (Eds.) Evaluating social programs and problems: Visions for the new millennium. Hillsdale, NJ: Erlbaum.
Fetterman, D.M. and Bowman, C. (2001). Experiential Education and Empowerment Evaluation: Mars Rover Educational Program Case Example. Journal of Experiential Education.
Fetterman, D.M. (2002). Web surveys to Digital Movies: Technological Tools of the Trade. Educational Researcher, 31(6):29-37 or http://aera.net
Fetterman, D.M. (1998). Teaching in the Virtual Classroom at Stanford University. The Technology Source.
Fetterman, D.M. (1998). Webs of Meaning: Computer and Internet Resources for Educational Research and Instruction. Educational Researcher, 27(3):22-30.
Fetterman, D.M. (1998). Learning with and about technology: A middle school nature area. Meridian, 1(1)
Fetterman, D.M. (1996). Empowerment Evaluation: An Introduction to Theory and Practice. In Fetterman, D.M., Kaftarian, S., and Wandersman, A. (eds.) Empowerment Evaluation: Knowledge and Tools for Self-Assessment and Accountability. Newbury Park, CA: Sage.
Fetterman, D.M. (1996). Conclusion: Reflections on Emergent Themes and Next Steps. In Fetterman, D.M., Kaftarian, S., and Wandersman, A. (eds.) Empowerment Evaluation: Knowledge and Tools for Self-Assessment and Accountability. Newbury Park, CA: Sage.
Fetterman, D.M. (1996). Videoconferencing On-Line: Enhancing Communication Over the Internet. Educational Researcher, 25(4)
Fetterman, D.M. (1995). In Response to Dr. Daniel Stufflebeam’s: “Empowerment Evaluation, Objectivist Evaluation, and Evaluation Standards: Where the Future of Evaluation Should Not Go and Where It Needs to Go,” Evaluation Practice, June 1995, 16(2):179-199.
Fetterman, D.M. (1994). Gifted and Talented Education Program Evaluation. In Sternberg, R.J. (ed.) Encyclopedia of Human Intelligence. New York, NY: Macmillan Publishing Company.
Fetterman, D.M. (1994). The Terman Study. In Sternberg, R.J. (ed.) Encyclopedia of Human Intelligence. New York, NY: Macmillan Publishing Company.
Fetterman, D.M. (1994). Keeping Research on Track. New Directions for Program Evaluation. No. 63, Fall. San Francisco, CA: Jossey-Bass, pp. 103-105.
Fetterman, D.M. (1994). Empowerment Evaluation. Presidential Address. Evaluation Practice, 15(1):1-15.
Fetterman, D.M. (1992). Hevrah: Our Intellectual Community. Anthropology and Education Quarterly, 23(4): 271-274.
Fetterman, D.M. (1992) Evaluate Yourself. Storrs, CT: National Research Center on the Gifted and Talented.
Fetterman, D.M. (1991). Evaluation in Multi-Site and Multi-Focus Projects. Revitalizing Rural America: New Strategies for the Nineties. Georgia Center for Continuing Education. Athens, GA: The University of Georgia.
Fetterman, D.M. (1990). Health and Safety Issues: Colleges Must Take Steps to Avert Serious Problems. The Chronicle of Higher Education, March 21, A48.
Fetterman, D.M. (1989). Anthropology Can Make a Difference. In Trueba, H., G. Spindler, and Spindler, L. (Eds.) What Do Anthropologists Have to Say About Dropouts? New York, NY: Falmer Press, 1989.
Fetterman, D.M. (1988). Stanford Special Review on Health and Safety Phase II: A Report on Allegations. Internal Audit Department. Stanford, CA: Stanford University.
Fetterman, D.M. (1988). Gifted and Talented Education. In Gorton, R.A., Schneider, G.T., and Fisher, J.C. (Eds.) Encyclopedia of School Administration and Supervision. Phoenix, AZ: Oryx Press.
Fetterman, D.M. (1986). Operational Auditing in a Teaching Hospital: A Cultural Approach, Internal Auditor, 43(2):48-54.
Fetterman, D.M. (1986). Evaluating Organizational Culture in a Teaching Hospital: The Use of Cultural Concepts and Techniques. In K. Sedgwick (Ed.), Association of College and University Auditors. Logan, Utah: Utah State University.
Fetterman, D.M. (1982). Ibsen’s Baths: Reactivity and Insensitivity (A misapplication of the treatment-control design in a national evaluation). Educational Evaluation and Policy Analysis, 4 (3):261-279.
Fetterman, D.M. (1981). Protocol and Publication: Ethical Obligations. Anthropology and Education Quarterly, 7(1):82-83.

RADIO INTERVIEWS (recent at: http://www.davidfetterman.com/RadioInterviews.htm)

Empowerment Evaluation in the Digital Villages (book), KAZI FM, Houston, Texas, March 29, 2013.
Chronicle of Philanthropy article about evaluation and nonprofit survival (Chronicle), WPFM FM, Washington, D.C. March 25, 2013.
Empowerment Evaluation in the Digital Villages (book), Kathryn Zox Show, March 13, 2013.
Empowerment Evaluation in the Digital Villages (book), Money Matters Network, Host Stu Taylor, January 28, 2013.
Empowerment Evaluation in the Digital Villages (book), WKXL-AM, Concord, New Hampshire, Host Bill Kearney, January 17, 2013.
Empowerment Evaluation in the Digital Villages (book), WPHM-AM Detroit, Host Paul Miller, January 14, 2013.
Empowerment Evaluation in the Digital Villages (book), Business Matters Radio, Host Thomas White, January 14, 2013.

BLOGS (selected)

Fetterman, D.M. (2014) David Fetterman on Google Glass Part I: Redefining Communications. AEA365. American Evaluation Association. http://aea365.org/blog/david-fetterman-on-google-glass-part-i-redefining-communications/ (April 17.)
Fetterman, D.M. (2014) David Fetterman on Google Glass Part II: Using Glass as an Evaluation Tool. AEA365. American Evaluation Association. http://aea365.org/blog/david-fetterman-on-google-glass-part-ii-using-glass-as-an-evaluation-tool/ (April 18.)
Fetterman, D.M. (2013). In These Uncertain Times, Charities Need a Survival Plan. The Chronicle of Philanthropy. March 10. http://philanthropy.com/article/In-These-Uncertain-Times/137741/
Fetterman, D.M. (2013). Surviving the Fiscal Cliff: The one thing every nonprofit should do in the face of federal tax increases and spending cuts. Stanford Social Innovation Review. http://www.ssireview.org/blog/entry/surviving_the_fiscal_cliff (January).
Fetterman, D.M. (2012). Empowerment Evaluation in the Digital Villages. Stanford Social Innovation Review. http://www.ssireview.org/articles/entry/empowerment_evaluation_in_the_digital_villages_hewlett_packards_15_million (December)
Fetterman, D.M. (2012). Corporate Philanthropy Tackles the Digital Divide. Stanford Social Innovation Review. http://www.ssireview.org/blog/entry/corporate_philanthropy_tackles_the_digital_divide (November)

ENCYCLOPEDIA (selected): The International Encyclopedia of Education and Encyclopedia of Human Intelligence

Dr. Ann E.K. Um
Dr. Ann E.K. UmPresident and CEO
EDUCATION

Doctorate Degree, Columbia University
Master’s Degree, Stanford University
Master’s Degree, Columbia University

EXPERIENCE

Harvard Medical School, DFCI, Research Data Manager
Harvard Medical School, Brigham and Women’s Hospital, Data Science Manager
The University of Texas, Assistant Professor

PUBLICATIONS (selected)

Autonomy Support, Self-Concept, and Mathematics Performance: A Structural Equation Analysis. Saarbrucken, Germany: VDM Verlag, 2010.
Motivation and Mathematics Achievement: A Structural Equation Analysis, Saarbrucken. Saarbrucken, Germany: VDM Verlag, 2008.
Motivation and Mathematics Performance: A Structural Equation Analysis. Michigan, Ann Arbor: ProQuest, 2006.
Motivation and Mathematics Performance: A Structural Equation Analysis (doctoral dissertation). Columbia University, New York, 2005.

PRESENTATIONS (selected)

Motivation and Mathematics Performance: A Structural Equation Analysis, National Council on Measurement in Education, Montreal, Quebec, Canada, 2005.
Comparing Eighth Grade Diagnostic Test Results for Korean and American Students, National Council on Measurement in Education, Chicago, Illinois, 2003.

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