June2015 by Editor IJITCE

ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING JUNE 2015 VOL- 5 NO -6

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UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. Effective government service provision benefits from the support of rigorous measurement techniques. Data Envelopment Analysis (DEA) is an analytical tool that can assist in the identification of best practices in the use of resources among a group of organisations. Such identification can highlight possible efficiency improvements that may help agencies to achieve their potential.DEA can be a very useful analytical technique by providing an important first step tool in comparative analysis. But users also need to recognise its limitations as an input to the development of public policy. Its theoretical predictions of potential efficiency gains may not be translatable into actual gains when factors such as service quality, fundamental differences between individual services and the costs of implementing changes are fully accounted for. Non-efficiency objectives such as access and equity are also important policy considerations for government, against which efficiency benefits will inevitably be balanced. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.6 JUNE 2015 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,RuaItapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.6 JUNE 2015

Contents An Enhanced Efficiency Technique for Hospitals at Karur using Data Envelopment Analysis A.Selvakumar & S.Prasath ……………….……………………………….…………………………. [277]

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An Enhanced Efficiency Technique for Hospitals at Karur using Data Envelopment Analysis A.Selvakumar Assistant Professor, Department of Commerce with Computer Application, Sree Saraswathi Thyagaraja College, (Autonomous), Pollachi Email: deesel@rediffmail.com S.Prasath Assistant Professor, Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India. Email: softprasaths@gmail.com Abstract— The main purpose of this paper is to observe the technical efficiency of hospitals located in karur area using Data Envelopment Analysis. Data envelopment analysis is a non-parametric method for measuring and evaluating performance of set of entity called Decision Making Units. This method is capable to compact with multi-dimensional environment of input and output variables. A few of the local hospitals situated in karur area are measured for efficiency analysis. The several significant of inputs and outputs of these hospitals have been integrated to reveal the method. Finally, the frontiers have been identified and compared on the basis of which lagging hospitals have been given suggestion for improving their services. Keywords— DEA, DMU, Technical efficiency, Scale efficiency and constant return to scale, variable return to scale.

1. INTRODUCTION Health care is a sector that has to provide services related to the general public and take care of not just their needs to make them well but also to provide the same in a better manner. Health care organizations are the ones that take care of people in their illness and so they have to be hygienic, clean and provide better services. The services provided by Health Care Centres and the Quality of services provided are the main basis on which they have to be judged, even their name is spread through word of mouth if they provide with good services. The efficiency of a Health care unit depends on the quality of service provided and to measure the same we are using Data Envelopment Analysis (DEA) as a tool. Data envelopment analysis is a method that is based on analyzing the statistical data based on a set of inputs called Decision Making Units, and mostly used for measuring and evaluating performance of the data provided. Data Envelopment Analysis has been done by taking the following inputs – Number of Doctors, Number of Nurses, Number of beds, Number of Paramedical Staff and the Number of wards. The output of the provided input are- Average number of OPD patients per day and Average number of patients admitted per day.

2. REVIEW Benneyan et al. [1] developed DEA to identify the countries with most efficient healthcare systems were identified on clinical outcomes, health adjusted life years, access, equity, safety and resources. The results reported only few countries to be efficient and suggested improvement measures. Cooper [2] introduced the technique DEA. It is a non-parametric technique to find the relative efficiencies of decision-making units (DMUs) incorporating multiple inputs and multiple outputs. Jain et al. [3] described DEA for performance measurement and target setting in two manufacturing companies of discrete production systems. Various DEA models were applied and tested the factors contributing to lower efficiencies were identified in similar circumstances. Jong Jooet al. [4] revealed DEA for benchmarking and illustrate variable selection using Return on Asset (ROA) for comparing efficiency of companies in the same industry. Moreover, a framework was suggested for selecting variables for performance measurement and benchmarking which includes general merchandisers. Shafiee et al. [5] evaluated the efficiency of an Iranian Bank using dynamic Slack Based Model (SBM) of Data Envelopment Analysis. They measured the efficiency of bank branches for more than two periods considering net profit as a good link and loan losses as a bad link and it compared the dynamic SBM efficiency with the static efficiency. Sherman [6] developed DEA as a managerial audit tool for resources allocation and analytic review of efficiency when applied to given set of situations. They also interpreted strengths and limitations of DEA techniques. The study was not only to evaluate the relative efficiency of non profit and public sector Decision Making Units (DMUs) but also extends to profit sector class. Tripathy et al. [7] applied DEA to pharmaceutical firms in India for measuring the efficiency. The analysis of 90 sample firms was undertaken to measure the technical efficiency. The findings showed that performance of large number of sample firms was not optimal and the average efficiency of the R&D intensive firms is greater than that of non R&D firms.

277

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.6 JUNE 2015 Weber [8] published work on performance measurement of vendors. It was recognized that vendor selection is a multiple criteria decision-making problem. This work demonstrated that how DEA could be used to measure the performance of vendors on multiple criteria. Zhou et al. [9] conducted a literature review on the importance of energy and environmental modeling techniques. The various DEA techniques have been highlighted and a large number of publications have been classified in this field. They observed that the benchmarking of electricity utilities accounts was common for the most studies. Jat [10] conducted several studies on hospitals. Recently, a research was conducted in public district hospitals in Madhya Pradesh to measure the technical efficiency. Data from 40 district hospitals were collected and DEA was performed with return to scale assumptions. Out of all the hospitals in this study, 50% were technically efficient and the rest were inefficient according to the best practice frontiers.

Table.1 depicts the efficiency score of hospitals using CRS and VRS models applying DEA solver. In this section our analysis is a three-step procedure. CRS model is used to measure technical efficiency and then VRS model is used to calculate technical efficiency. This helped us to identify inefficient hospitals on account of inefficient operations. Finally scale efficiency is computed by taking the ratio of CRS and VRS. It provides information about those hospitals that are operating under disadvantageous conditions. This characteristic of each hospital can scale the input or output linearly without increasing or decreasing the efficiency.

3. Methodology To measure the efficiency of hospitals, the technique used is Data Envelopment Analysis and the technique for measurement of relative efficiency of some organizations such as hospitals and schools. The linear programming technique is used in DEA to calculate the relative efficiency for each hospital. Efficiencies vary between 0 and 1. Hospitals having a score of 1 or 100% are technically efficient and hospitals having scores of less than 1 are technically inefficient. 3.1 DEA Analysis A comparative study of input oriented CRS and VRS models is done. Firstly, the efficiencies of DMUs are measured using CRS and VRS model. The CRS model works on the concept of constant-return-to-scale. It computes the efficiency scores, which defines Technical Efficiency measures the firms success in producing maximum output from a given set of inputs. The VRS model works on the concept of variable-return-to-scale. It computes the efficiency scores, which defines Pure Technical Efficiency. 4. Analysis and Discussion DEA model applied to all output-oriented for compute efficiency. The data for study has been collected from private hospitals at Karur region. The input measures used in this study are number of doctors, number of nurses, number of paramedical staff, number of beds, number of wards whereas the output measures are average number of OPD patients per day and average number of patients admitted per day are shown in Table 1. Nurse

Staff

Beds

Wards

OPD Patients

Patients admitted

Hospitals

Doctor

100

Table.1 Input-output variables 278

Table.2 Efficiency of CRS model

DMU Name

InputOriented CRS Efficiency

DMU No.

Optimal Lambdas with Benchmarks

Sum of lambdas

RTS

0.41270

0.397

Increasing

0.381

1.00000

1.000

Constant

1.000

0.42015

0.310

Increasing

0.023

1.00000

1.000

Constant

1.000

1.00000

1.000

Constant

1.000

0.92647

0.515

Increasing

0.463

0.37736

0.358

Increasing

0.189

1.00000

1.000

Constant

1.000

1.00000

1.000

Constant

1.000

DMU No.

Table.3 Efficiency of VRS model InputOriented Optimal Lambdas DMU VRS with Benchmarks Efficiency Name A

1.00000

1.000

1.00000

1.000

1.00000

1.000

1.00000

1.000

1.00000

1.000

1.00000

1.000

0.72649

0.323

1.00000

1.000

1.00000

1.000

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.6 JUNE 2015 Table.4 Input-Oriented CRS model Slacks Input Slacks DM U No.

DMU Nam e

Doctor

0.07937

0.00000

3.13719

0.00000

1.33824

1.15094

0.00000

Nurse

Staff

Beds

Wards

0.4127 0 0.0000 0 0.4737 4 0.0000 0 0.0000 0 0.0000 0 0.0000 0 0.0000 0 0.0000 0

0.8412 7 0.0000 0 0.0000 0 0.0000 0 0.0000 0 0.8750 0 0.0000 0 0.0000 0 0.0000 0

0.0000 0 0.0000 0 9.5862 8 0.0000 0 0.0000 0 0.0000 0 0.3396 2 0.0000 0 0.0000 0

1.3015 9 0.0000 0 0.0000 0 0.0000 0 0.0000 0 0.6176 5 1.2452 8 0.0000 0 0.0000 0

Output Slacks OPD patient s

[2]

[3]

0.00000 0.00000 0.00000

[4]

0.00000 0.00000 8.41176

[5]

0.35849 0.00000 0.00000

[6]

Table.5 Input-Oriented VRS model Slacks Input Slacks DM U No.

DM U Nam e

Docto r

Nurse

Staff

Beds

Ward s

0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.949 96 0.000 00 0.000 00

1.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00

2.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00

0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00

3.000 00 0.000 00 0.000 00 0.000 00 0.000 00 0.000 00 1.016 39 0.000 00 0.000 00

Output Slacks OPDpatie nts

[7]

30.00000 0.00000 0.00000

[8]

0.00000 0.00000

[9]

0.00000 13.18809 0.00000 0.00000

5. CONCLUSION In this paper, focused on measurement of the efficiency of hospitals located at karur and ranked them according to their scale efficiency. DEA models have been applied and proved to be a useful approach for efficiency measurement. Two models of DEA namely CRS and VRS were undertaken to obtain the efficiency scores of Karur hospitals. These scores have been relatively compared and scale efficiency is computed using the ratio of CRS and VRS. A comprehensive analysis of input and output variables is considered while assuming scale. This type of analysis might be useful for benchmarking consider to health care system.

[1]

6. REFERENCES Benneyan, J., Ceyhan, M.E., Sunnetci, A. (2007), “Data envelopment analysis of national healthcare systems and their relative efficiencies”, International Conference on Computers and Industrial Engineering, Vol.,No., PP.251-261. 279

Cooper, W.W., Seiford, L.M., Zhu, J.( 1990) “Recent Developments in DEA : The mathematical programming approach to frontier analysis”, Journal of Econometrics, Vol. 46, No.1-2, PP.7-38 Jain, S., Triantis, P.K., Liu, S. (2011) “Manufacturing performance measurement and target setting: A Data Envelopment Analysis approach”, European Journal of Operational Research, Vol.214, No.3, PP.616-626. Joo, S.J., Nixon,D., Stoeberl, P.A. (2011) “Benchmarking with data envelopment analysis: a return on asset perspective”, An International Journal, Vol.18, No.4, PP.529-542. Shafiee, M., Sangi, M., Ghaderi, M. (2013) “Bank performance evaluation using dynamic DEA: A slacks-based measure approach”, Journal of Data Envelopment Analysis and Decision Science, Vol.2013, No.26, PP.1-12. Sherman, H.D. (1982). Data Envelopment Analysis as a new managerial audit methodology-test and evaluation. Cambridge , Mass: Massachusetts Institue of Technology. Tripathy, I.G., Yadav, S.S., Sharma, S. (2012) “Measuring the efficiency of Pharmaceutical firms in India: An application of Data Envelopment Analysis and Tobit estimation”, Defence Scientific Information & Documentation Centre (DESIDOC) Journal of Library and Information Technology ,Vol.32, No.3, PP.228-232 Weber, C.A. (1996) "A data envelopment analysis approach to measuring vendor performance", Supply Chain Management: An International Journal, Vol. 1, No. 1, PP. 28-39 Zhou, P., Ang, B.W., Poh, K.L. (2008) “A survey of data envelopment analysis in energy and environmental studies”, European Journal of Operational Research, Vol.189, No. 1, PP.1-18.

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