Literature DB >> 3148679

A case mix simulation decision support system model for negotiating hospital rates.

M E Hatcher1, C Connelly.   

Abstract

The institution of prospective payment systems by many health care insurers has drawn increased attention to case-based financial planning in hospitals. When hospital revenues are directly linked to patient diagnoses rather than to the types and quantities of services supplied to patients, managers must be aware of the financial implications of different case mixes and must be prepared to influence insurers' price structures. A case-based financial planning model is presented here for the purpose of assisting managerial decision making in the strategic areas of case mix planning and pricing. The computerized model characterizes hospitals as product manufacturers, the product being discharged patients. Diagnosis serves to differentiate the "products"; however, diagnoses are grouped by payor and similar treatment cost experiences to create a limited set of managerially meaningful case types. Diagnostic and treatment costs are also aggregated to facilitate the modeling of the hospital production process. The computerized model projects the number of patients of each case-type and total patient volume, based on estimated patient volume growth rates. The model also projects prices and contribution margins for each case-type, as well as total contribution to hospital overhead. Testing the model with a hypothetical example of a hospital strategic planning problem demonstrates the model's potential as a decision-making aid in case mix planning and case-type pricing. It also reveals several model shortcomings that require further developmental effort.

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Year:  1988        PMID: 3148679     DOI: 10.1007/bf00992684

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  16 in total

Review 1.  The state of the art of financial modeling.

Authors:  J R Coleman
Journal:  Hosp Health Serv Adm       Date:  1980

2.  How to develop or buy a computerized financial planning model.

Authors:  F L Poggio
Journal:  Hosp Financ Manage       Date:  1979-11

3.  DRG payments and net contribution variance analysis.

Authors:  S Y Soliman; W L Hughes
Journal:  Healthc Financ Manage       Date:  1983-10

4.  Hospitals will continue to treat all DRGs to snare 'contribution margin'.

Authors:  A J Keegan
Journal:  Mod Healthc       Date:  1983-09

5.  Estimating hospital costs by diagnosis for population-based analysis.

Authors:  D S Shepard; D N Soule
Journal:  J Community Health       Date:  1981

6.  Decision support systems in hospitals.

Authors:  E Turban
Journal:  Health Care Manage Rev       Date:  1982

7.  Hospital case mix groupings and generic algorithms.

Authors:  D A Bertram; D N Schumacher; S D Horn; C J Clopton; J G Lord; C Chan
Journal:  QRB Qual Rev Bull       Date:  1982-01

8.  Measurement of case mix.

Authors:  J D Bentley; P W Butler
Journal:  Top Health Care Financ       Date:  1982

9.  Improved cost allocation in case-mix accounting.

Authors:  S V Williams; S A Finkler; C M Murphy; J M Eisenberg
Journal:  Med Care       Date:  1982-05       Impact factor: 2.983

10.  A conceptual model of the case-based payment scheme for New Jersey hospitals.

Authors:  J B Reiss
Journal:  Health Serv Res       Date:  1980       Impact factor: 3.402

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  6 in total

1.  Tutorial on technology transfer and survey design and data collection for measuring Internet and Intranet existence, usage, and impact (survey-2000) in acute care hospitals in the United States.

Authors:  M Hatcher
Journal:  J Med Syst       Date:  2001-02       Impact factor: 4.460

Review 2.  Information technology in the future of health care.

Authors:  Myron Hatcher; Irene Heetebry
Journal:  J Med Syst       Date:  2004-12       Impact factor: 4.460

3.  Decision-making with and without information technology in acute care hospitals: survey in the United States.

Authors:  M Hatcher
Journal:  J Med Syst       Date:  1998-12       Impact factor: 4.460

Review 4.  Uniqueness of Group Decision Support Systems (GDSS) in medical and health applications.

Authors:  M Hatcher
Journal:  J Med Syst       Date:  1990-12       Impact factor: 4.460

5.  Survey of acute care hospitals in the United States relative to technology usage and technology transfer.

Authors:  M Hatcher
Journal:  J Med Syst       Date:  1997-10       Impact factor: 4.460

Review 6.  Voting and priorities in health care decision making, portrayed through a group decision support system, using analytic hierarchy process.

Authors:  M Hatcher
Journal:  J Med Syst       Date:  1994-10       Impact factor: 4.460

  6 in total

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