Healthcare Management Engineering: What Does This Fancy Term Really Mean? The Use of Operations Management Methodology for Quantitative Decision-making in Healthcare Settings

Citation

(2012), "Healthcare Management Engineering: What Does This Fancy Term Really Mean? The Use of Operations Management Methodology for Quantitative Decision-making in Healthcare Settings", Leadership in Health Services, Vol. 25 No. 2. https://doi.org/10.1108/lhs.2012.21125baa.008

Healthcare Management Engineering: What Does This Fancy Term Really Mean? The Use of Operations Management Methodology for Quantitative Decision-making in Healthcare Settings

Healthcare Management Engineering: What Does This Fancy Term Really Mean? The Use of Operations Management Methodology for Quantitative Decision-making in Healthcare Settings

Article Type: Recent publications From: Leadership in Health Services, Volume 25, Issue 2

Please note these are not reviews of the titles given. They are descriptions of the book, based on information provided by the publishers.

Healthcare Management Engineering: What Does This Fancy Term Really Mean? The Use of Operations Management Methodology for Quantitative Decision-making in Healthcare SettingsAlexander KolkerSpringer2012ISBN 978-1-4614-2067-5

Keywords: Healthcare management engineering, Decision-making in healthcare, Healthcare leadership

The book illustrates in depth a concept of healthcare management engineering and its domain for hospital and clinic operations. The predictive and analytic decision-making power of management engineering methodology is systematically compared to traditional management reasoning by applying both side by side to analyse 26 concrete operational management problems adapted from hospital and clinic practice. The problem types include:

• •

  clinic, bed and operating rooms capacity;

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  patient flow;

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  staffing and scheduling;

• •

  resource allocation and optimization;

• •

  forecasting of patient volumes and seasonal variability;

• •

  business intelligence and data mining; and

• •

  game theory application for allocating cost savings between cooperating providers.

Detailed examples of applications are provided for quantitative methods such as discrete event simulation, queuing analytic theory, linear and probabilistic optimization, forecasting of a time series, principal component decomposition of a data set and cluster analysis, and the Shapley value for fair gain sharing between cooperating participants. A summary of some fundamental management engineering principles is provided.

The goal of the book is to help to bridge the gap in mutual understanding and communication between management engineering professionals and hospital and clinic administrators.

The book is intended primarily for hospital/clinic leadership who are in charge of making managerial decisions. This book can also serve as a compendium of introductory problems/projects for graduate students in healthcare management and administration, as well as for MBA programs with an emphasis in healthcare.

Contents include:

1. 1.

   Traditional Management and Management Engineering:

2. 2.
   • •

     Dynamic supply and demand balance problems.

   • •

     Discrete event simulation methodology: what is a discrete event simulation model and how does a simple model work?

   • •

     Queuing analytic theory: its use and limitations.

   • •

     Capacity problems.

   • •

     Scheduling and staffing problems.

3. 3.

   Linear and probabilistic resource optimization and allocation problems:

4. 4.
   • •

     Optimization of patient service volumes.

   • •

     Optimization of clinical unit staffing for 24/7 three-shift operations: is staffing cost minimized?

   • •

     Resident physician restricted work hours: optimal scheduling to meet the institute of medicine new workload recommendations.

   • •

     Optimized pooled screening testing.

   • •

     Projected number of patients discharged from ED.

5. 5.

   Forecasting time series:

6. 6.
   • •

     Forecasting patient volumes using time series data analysis.

   • •

     Forecasting time series with seasonal variation.

7. 7.

   Business intelligence and data mining:

8. 8.
   • •

     Multivariate database analysis: what population demographic factors are the biggest contributors to hospital contribution margin?

   • •

     Cluster analysis: which zip codes form distinct contribution margin groups?

9. 9.

   The use of game theory:

10. 10.
    • •

      Is distributing of savings between cooperating providers fair? The use of the Shapley value concept.

11. 11.

    Summary of some fundamental management engineering principles.