A Soft Systems Methodology Model for Clinical Decision Support Systems (SSMM-CDSS)

Grace S Loo

The University of Auckland Auckland New Zealand

Dept of Management Se amp Information Systems

glooaucklandacnz Fax +64 9 5212299

Abstract

A systematic approach for the development of clinical decision support systems is essential for an effective design and implementation of medical information system This research paper proposes the use of Soft Systems Methodology (SSM) as a tool for analysing clinical support system of medical information system using various models

From the SSM model so developed a quantitative survey is designed and used for analysing the attitudes of doctors towards the use of clinical decision support system Useful findings have been obtained from a research project conducted in Malaysia These results provide useful pointers for the development of national health information system in Malaysia and some other countries of similar background and they serve the needs to minimise wastage of resources and optimise benefits generated from the use of clinical decision support system

Keywords information system Soft Systems Methodology

Clinical decision support systems medical

1 Introduction

Currently many countries in North America Australasia and Europe are actively engaged in developing its medical information system (MICIS) to keep pace with the progress made in other aspects of national and international development [Barahona et al 1994 Classen 19981 The Good European Health Model (GEHR) later known as the Good Electronic Health Model started in the early nineties [Shortliffe 19991 is one such example In Australia Malaysia and New Zealand medical specialists and information systems researchers are actively working on MICIS to better the quality of life of their people

New Zealand though a small nation follows closely with other developed countries in the development of its MICIS Since the early eighties its Ministry of Healamp has embarked on certain national health strakgies in a structured manner Information management and

httpwwwgehrorg httpwwwmohgovtnznzhshtml [A King 20001

1529-418801$1000 0 2001 IEEE

Philip C H Lee Higher Education Programme Institute

Kuala Lumpur Malaysia philleetm net my

technology is vital for providing the ability to exchange high-quality information between partners in healthcare processes ultimately to achieve better health outcomes Clinical Decision Support Systems (CDSS3) provides a natural choice of information system tool

Malaysid develops its health care system in its own way In various parts of the country there is an integration of western medical science and the traditional health practices (alternative medicine) during the last decade However CDSS has not gained as much attention among the doctors as in some western countries by the late nineties This research project (initiated in Malaysia in 1999) highlights the associated issus that arise and proposes some solutions for consideration by clinicians and researchers It investigates the attitudes of doctors socio-technical issues and past implementation efforts and offers suggestions for effective adoption of CDSS for MICIS

In this paper section 2 discusses clinical decision support systems (CDSS) Section 3 introduces the background of Soft Systems Methodology (SSM) and the usage of SSM in our present project SSMM-CDSS A description of a proposed model together with its implementation in our project is given in section 4 and the preliminary results presented in section 5 This is followed by the conclusion future work and acknowledgment in the final section

2 Clinical Decision Support System A Decision Support System (DSS) is an interactive

computer based systems that assist decision makers to utilise data models solvers and user interfaces to solve semi-structured and or unstructured problems [ Sprague 19801 In this research project CDSS is defined to be any software (DSS) designed to directly aid clinical decision making Useful characteristics of the patient are made available through the use of specific CDSS to clinicians for further examination [Hunt et al 19981

CDSS will be treated as one subject (singular) The author G S Loo is familiar with both New Zealand and Malaysian health systems having lived in both countries for long periods besides substantial periods in Europe and Aurtralia The information background of the health systems in both Malaysia and New Zealand are drawn upon in this project

httpNwwwthestarcommy Useful site on background of Malaysian culture and health system

909

CDSS does not make decisions but supports diagnostic decisions of doctors CDSS is viewed as information technology defined as mechanisms to implement desired information handling in the organisation [Avgerou amp Comford 19931 Thus CDSS also supports work procedures and organisational goals There are studies done on CDSS in the areas of drug use and preventive medicine The quality of such studies has been increasingly improved over the years since the late 1950s [Shortliffe 19871 Some CDSS incorporates fuzzy logic to overcome restrictions of diagnostic programs Among researchers and developers fussy logic is now accepted as the technique to represent approximation in knowledge representation and user interface design [Loo amp Lee 20001 Pathophysiologic reasoning has also been used torepresent temporal evaluation of single and multiple disease process [Mazoue 19901

Mean range 1 OO - 174

Hunt et al [ 19981 made a review of their studies that spanned over a twenty-five years concluded that the CDSS does improve the performances of health practitioners but to a lesser extent the patients outcome The findings of a case study reported in our project indicate that the adoption of CDSS in Malaysia do receive less favourable comments from some participants of the study These unfavourable comments could be partly attributed to the fact that some participants are misinformed by the study of Hunt et al

Interpretation of mean for analysis vew favourable

-

3 Soft Systems Methodology In the early seventies when software developers were

faced with ilLdefined problems or situations that required information systems development there were very few established methods of systems engineering available At that time the systems engineering defining assumption was that the system of concern exists could be named and could be manipulated in the interests of efficiency [Checkland 19811 This basic assumption held well if the optimum solution could be found for a particular situation However this was not always the case with reaLworld situations

Soft System Methodology (SSM) evolved then to meet that need when no optimum solution was available SSM placed emphases on peoplek perception of reality and worked with the notion of a problem situation in which various players might perceive various aspects to be problematical SSM had a system of enquiry hat was

Table 41 Mean categorisation used to analyse data

v o t e Lower mean score represent more favourable attitude towards

CDSS Mean score in FigA41

formally expressed to allow learning and make sense of complex situations to enable purposeful actions

4 SSMM Design and Implementation A Soft Systems Methodology Model (SSMM) was

proposed to provide a modelinfastructure for the overall design for the present project using a combination (ie triangulation) of several data collection methods McNeil [ 19851 the data collection methods used are SSM official statistics and survey that includes observation and interview Each data collection method had its own advantages and limitations The strength of one method was invoked to make good the weakness of another A quantitative approach (using closeended survey and official statistics) and a qualitative approach (using SSM) were employed to take account of both data values and information on behaviour SSM was adopted to provide a seven-stage model framework on one hand and to serve as a mouldable methodology for investigating unstructured situations on the other An emancipatory research strategy was utilised to resolve conflicting viewsperceptions in the human activity system The qualitative approach (SSM) was employed for handling quality data while the quantitative approach was adopted to investigate the case study of the Malaysia doctors community

41 Model Infrastructure

SSMM used in this project had the following thresstep framework

(i) Formulating some models of human activity systems that were relevant to the problem situation studied

(ii) Using the formulated modcis in step (i) to analyse the various perceptions of the real world by a process of comparison

Invoking the results of comparison obtained in step (ii) to prescribe a set of purposeful actions to take to reformulate the original problem in a manne that will yield an acceptable feasible solution

(iii)

In this infrastructure four perspectives were considered (a) People perspective as studied from attitudinal survey instrument (b) Organisation technology and function perspectives to be considered in a feasibility study of the viability of CDSS implementation (c) Perspective of data as collected from interviews with medical practitioners and observations in Malaysian hospitals (d) The perspective of organisational theory to be used to analyse reasons for the participantsrsquoslow adoption and their resistance to change

Holons and surveys used are elaborated below

Holons As defined by SSM holons represented human activity systems relevant to real purposeful actions and were built from some declared perspectives or worldviews [Checkland amp Scholes 19901 They were used here for identifying different perspectives of the purposeful

910

activities of CDSS In SSMM holons were defined for the adoption situation of CDSS in Malaysia Surveys The present study was conducted to cover three occupational groups in Malaysia namely medical practitioners with clinical responsibilities medical students and hospitalhealth departmental managers These groups were most affected by the adoption of CDSS A survey instrument was developed on the basis of a series of interviews using SSM adaptation of survey design from past studies and the results of pilot testing Opinions and attitudes were summarized from the sample interviews A sample size of 120 was considerd adequate for the present study through examining the results from the pilot survey For the final survey some initial questions were restructured for clarity after feedbacks were received from pilot study Likert scale was used to measure attitudes Both personal interviews and email were used to cover different parts of Malaysia

Data were analysed using the standard SPSS statistical package (Release 75 1996) With the help of a SPSS manual the reliability of the scale was calculated as CronbachB alpha Data obtained from open -ended questions were categorized into relevant themes for ease of analysis

42 Feasibility study on adoption of clinical decision support system in Malaysia

This section is intended to guide the reader in considering whether it is worthwhile to construct amedical information system with CDSS in Malaysia The aim here is not to provide objective criteria for determining the viability of CDSS from the organisation standpoint The emancipatory research strategy acbpted here entails seeking information requirements of all users of the information system It is intended to answer the question ldquo How feasible is CDSS for use by different people in the information system environment rdquo The following discussions consider the technical legal organizational social and economic perspectives as outlined by Avgerou and Cornford (1 993) Some details of the discussions are given in the following subsections

421 Measures of performance

User requirements and alterntive systems were identified prior to assessing the feasibility of the systems Two systems are considered in this project and they were (a) the diagnosis of patients without CDSS currently and (b) the proposed system with CDSS In line with the emancipatory research strategy negotiation of user requirements was undertaken when deriving the primary task model using SSM The result was used to define performance measures for the feasibility study as advocated by Wilson B (1984)

The 5 Es

Elegance for monitoring and controlling h e system Fig Al in the appendix presents these five E criteria to measure the performance of the five aspects of a system considered in the feasibility study

422 Primary task model

The root definition is formulated as lsquoA government owned system manned by hospital director and management team to enable clinical decision support and learning among doctors and medical students by using artificial intelligence techniques to achieve goals of diagnoses accuracy and hospital efficiency in the bht of computer literacy and doctor-patient relationshiprdquo

The primary task root de3nition for CDSS was formulated after CATWOE analysis of Figure A2 was examined It follows the schema h system to do X by Y in order to achieve Zrsquoby Checkland and Scholes (1990) after a synthesis of various Weltanschauung is obtained Two transformation processes (X) were adopted in a relationship where diagnosis accuracy dominates learning The minimum activities necessary to meet the requirements were assembled and sbwn in a model in FigA3 Based on the defined infrastructure models and feasibility study some findings are presented below

5 Project Findings The survey found that generally Malaysian doctors have

attitudes ranging from unfavourable to neutral towards CDSS Many had misconceptions that CDSS would replace them and pose a threat to them and patients (as in Fig 51 below) The analysis showed they had low awareness and were willing to be exposed to this technology providing the possibility of aparadigm shift towards adoption in the future (Fig5234) Doctors intuitively employ complex decision making strategies based on common sense instead of fixed organisational and medical guidelines While the doctor would still be able to decide on the course of action to be undertaken CDSS would present the standard decision approved by the organisation

Attinudes of dWerent medical occupational groups about the use of CDSS in Malaysia

General COlnpiXw aientaDw Job 6Eurily O ~ e s s i o n a l h p a c l OMana9smenianem

Wilson B (1984) advocated the use of 3 Es (Efficacy EfficiencyEffectiveness) to measure the performance of the alternatives for a given situation Checkland and Scholes (1 990) added two more E criteria Ethicality and

Fig 51 Graph comparing attitudes among different occupational groups

911

Lack of exposure 14 (23) Insufficient funds 9 (15) System is not cost effective 7(11) Bureaucracy red tape 7 (1 1) Low computer accessibility 6 (10) Low IT literacy among doctors 5 (8) Lack of system with good research 4 (7) Few experienced respected CDSS developers 4 (7) No time to learn new technology 2 (3) Others 4 (5)

Total 62 (1 00)

Fig 52 Reasons cited by respondents for slow adoption of CDSS

Theme No ()

Improve productivity 7 (35) Access of information for doctors 6 (30) Public health education 4 (20) Improve quality of decisions 3 (15)

Total 20 (1 00)

Fig 53 Reasons CDSS can be beneficial

if networked with patients on the Internet

Theme No ()

Insufficient medical knowledge of patients Absence of clinical evaluation 4 (18) Inaccuracy of system 2 (9) Others 4 (18)

12 (55 )

Total 22 (1 00)

Fig 54 Reasons CDSS can be detrimental

if networked with patients on the Internet

Among tangible economic benefits of CDSS would be the savings achieved by using less medical resources and time to arrive at a diagnosis as CDSS produced a tested fixed procedure to arrive at the optimum decision on a range of cases The project had shown that there were various interest groups with different needs and prospects of CDSS adoption were higher when these needs were met

6 Conclusion This project by avoiding the study of single variables

and instead focus on total situations (the technical social and organisational context) of information systems yield some useful lessons

The participative approach is essential for medical information systems development By involving users be part of the development process of CDSS they contribute to the functional requirements of the CDSS such as consultative participation representative participation and consensus participation The danger is that through the use of CDSS management may pressure the doctorsrsquo conformity to organisational goals and to reduce frequency of expensive investigational treatment

This research has highlighted the problems and hurdles facing CDSS Many new techniques and developments have emerged during the last one year It is time for researchers to explore the design and usage of flexible web-based decision support system generator utilising software agents to provide additional functionalities

Theme NO (lsquoYo)

Clinical trialsto improve accuracy 13 (28) Information about the programme pros and cons 11 (23) medical workshops and seminars about CDSS 8 (1 7) Prototype to try 4 (9) Public awareness (through mass media) 4 (9) Clear misconceptions of doctors 3 (6)

User-friendly programme 2 (4) Higher computer literacy rate 2 (4)

(CDSS replaces them)

Total 47 (1 00)

Fig 55 Ways to improve adoption of CDSS

7 Acknowledgement The authors appreciate the cooperation of numerous

medical specialists and health pesonnel who have very kindly been involved in interviews and the survey The supports of the University of Auckland (NZ) and the HELP Institute (Malaysia) in enabling this project to be conducted are gratefully acknowledged here

8 References 1 Avgerou C and Comford T 1993 Developing Information

SystemsConcepts Issues and Practise Basingstroke U K Macmillan

2 Barahona P amp Chnstensen JP 1994 ldquoKnowledge and decisions in Health Telematicsrdquo Proceedings EPISTOL Health Decision Support Netherlands 10s Press

912

3 Checkland P 1981 Systems Thinking Systems Practice Chichester UK John Wiley amp Sons

4 Checkland P and Scholes J 1990 Soft Systems Methodology in Action Chichester UK John Wiley amp Sons Ltd Classen D 1998 ldquoClinincal Decision Support Systems to Improve Clininal Practice and Quality of Carerdquo

Hunt DL Haynes RB Hanna SE Smith K ldquoEffects of computer based clinical decision support systems on physician performance and paient outcomes a systematic reviewldquo Journal of the American Medical

Loo G L and Lee 2000 ldquoAn Interface to Databases for Flexible Query Answering A FuzzySet Approachrsquo

5

JAMA Oct 21V28 N015 pp1360-61 6

ASSOC (JAMA) 1998 280 1339-1346 7

Efficacy

Efficiency

Effectivencss

Ethicality

Elegance

Joumal LNCS 1873 [ 1 Ith Int Conf UK DEXA 20001

8 Mazoue J G 1990 The Journal of Medicine and pp 654-663

Philosophy ldquoDiagnosis without doctorsrdquo Vol 15 pp 559-579

9 McNeill P 1985 Research Methods UK Routledge 10 Shortliffe E H 1987 JAMA ldquoComputer Programs to

Support Clinical Decision Makingrdquo 3 July Vol 258 No 1 1 Sprague R H 1980 ldquoA Framework for the Development of DSSrdquo MIS Quarterly

1 1 Weaver RR 199 1 Computers andMedical Knowledge The diffusion of decision support systems Colorado USA Westview Press

12 Wilson B 1984 Systems Concepts Methodologies and Applications Chichester UK John Wiley amp Sons

Able to represent knowledge base accurately using artificial intelligence

Reduce the use of medical resourccs through better diagnosis accuracy

Doctors havc more time for patient care when unneccssary mistakes are avoided

Provide option for doctors to deploy CDSS or involve them in dcvelopment

User friendly computer interface

9 Appendix

Fig Al The 5 Es to measure CDSS feasibility in this project

C Customersrsquo - doctors medical students

A lsquoActorsrsquo - hospital director and management team

T Tran sformation - need for diagnosis accuracy and efficiency6 need met via

W Weltanschauungrsquo - a belief that technology could reduce human errors

0 Ownersrsquo - government (Ministry of Health)

E Environment - relationship between doctor and patient computer literacy

processrsquo clinical decision support and learning using artificial intelligence

constraintsrsquo

Fig A2 The primary task root definition Root definition(Pictorialdiagrammatic representation of the situations$ entities(structures) proces ses relationships and isuues)

913

2 0 b s e r v e d i a g n o s i s and

4 D e f i n e its relation to C D S S g o a l s

8 D e f i n e criteria for e f f i c a c y e f f i c i e n c y e f f e c t i v e

9 Monitor 1 - 7

Fig A3 A rough model of major necessary subsystems for CDSS developmen

I Scale and item I Mean f SD I Reliability I General computer orientation 303 f 096 080 CDSS will increase the productivity of hospitals 274 f 086 CDSS creates hassles for clinical staff 291 f081 CDSS can do jobs better than people 358 f 101 In the long run CDSS decrease the hospitalk cost 294 f 093 Use of this technology is unavoidable 265 f 089 I am confident that CDSS will succeed 285 f 078 Frequent CDSS breakdown is anticipated= 392 f 073 Job security 220 f 099 Increased use of CDSS mean less work for people 264 + 094 CDSS will threaten my future where I work= 213 f 092 CDSS will reduce my job security= 213 f 085 CDSS will make me less useful as a worker 206 k 1 OO My job skills are rapidly becoming obsolete 205 f 1 I 1 Professional impact 270 f 086 CDSS will enhance opportunities for medical care 241 f 0 7 5 CDSS will free up time for more professional activities 288 f 093 CDSS will upgrade job descriptions 282 f 082 Management concern 311 k 100 Managements interest is to improve productivity 324 f 109 Without concern for employees= Management will involve people in 274 f 089 Planning for implementation When management explain CDSS plans they 347 f 102 will not tell us the whole story=

086

071

065

Fig A4 Attitudes of doctors towards CDSS analysed using SPSS programme

-Negatively worded items were reverse coded before data inalysis Scores ranged from 1 to 5 the lower the mean the more positive the response SD=standard deviation

9 14

activities of CDSS In SSMM holons were defined for the adoption situation of CDSS in Malaysia Surveys The present study was conducted to cover three occupational groups in Malaysia namely medical practitioners with clinical responsibilities medical students and hospitalhealth departmental managers These groups were most affected by the adoption of CDSS A survey instrument was developed on the basis of a series of interviews using SSM adaptation of survey design from past studies and the results of pilot testing Opinions and attitudes were summarized from the sample interviews A sample size of 120 was considerd adequate for the present study through examining the results from the pilot survey For the final survey some initial questions were restructured for clarity after feedbacks were received from pilot study Likert scale was used to measure attitudes Both personal interviews and email were used to cover different parts of Malaysia

Data were analysed using the standard SPSS statistical package (Release 75 1996) With the help of a SPSS manual the reliability of the scale was calculated as CronbachB alpha Data obtained from open -ended questions were categorized into relevant themes for ease of analysis

42 Feasibility study on adoption of clinical decision support system in Malaysia

This section is intended to guide the reader in considering whether it is worthwhile to construct amedical information system with CDSS in Malaysia The aim here is not to provide objective criteria for determining the viability of CDSS from the organisation standpoint The emancipatory research strategy acbpted here entails seeking information requirements of all users of the information system It is intended to answer the question ldquo How feasible is CDSS for use by different people in the information system environment rdquo The following discussions consider the technical legal organizational social and economic perspectives as outlined by Avgerou and Cornford (1 993) Some details of the discussions are given in the following subsections

421 Measures of performance

User requirements and alterntive systems were identified prior to assessing the feasibility of the systems Two systems are considered in this project and they were (a) the diagnosis of patients without CDSS currently and (b) the proposed system with CDSS In line with the emancipatory research strategy negotiation of user requirements was undertaken when deriving the primary task model using SSM The result was used to define performance measures for the feasibility study as advocated by Wilson B (1984)

The 5 Es

Elegance for monitoring and controlling h e system Fig Al in the appendix presents these five E criteria to measure the performance of the five aspects of a system considered in the feasibility study

422 Primary task model

The root definition is formulated as lsquoA government owned system manned by hospital director and management team to enable clinical decision support and learning among doctors and medical students by using artificial intelligence techniques to achieve goals of diagnoses accuracy and hospital efficiency in the bht of computer literacy and doctor-patient relationshiprdquo

The primary task root de3nition for CDSS was formulated after CATWOE analysis of Figure A2 was examined It follows the schema h system to do X by Y in order to achieve Zrsquoby Checkland and Scholes (1990) after a synthesis of various Weltanschauung is obtained Two transformation processes (X) were adopted in a relationship where diagnosis accuracy dominates learning The minimum activities necessary to meet the requirements were assembled and sbwn in a model in FigA3 Based on the defined infrastructure models and feasibility study some findings are presented below

5 Project Findings The survey found that generally Malaysian doctors have

attitudes ranging from unfavourable to neutral towards CDSS Many had misconceptions that CDSS would replace them and pose a threat to them and patients (as in Fig 51 below) The analysis showed they had low awareness and were willing to be exposed to this technology providing the possibility of aparadigm shift towards adoption in the future (Fig5234) Doctors intuitively employ complex decision making strategies based on common sense instead of fixed organisational and medical guidelines While the doctor would still be able to decide on the course of action to be undertaken CDSS would present the standard decision approved by the organisation

Attinudes of dWerent medical occupational groups about the use of CDSS in Malaysia

General COlnpiXw aientaDw Job 6Eurily O ~ e s s i o n a l h p a c l OMana9smenianem

Wilson B (1984) advocated the use of 3 Es (Efficacy EfficiencyEffectiveness) to measure the performance of the alternatives for a given situation Checkland and Scholes (1 990) added two more E criteria Ethicality and

Fig 51 Graph comparing attitudes among different occupational groups

911

Lack of exposure 14 (23) Insufficient funds 9 (15) System is not cost effective 7(11) Bureaucracy red tape 7 (1 1) Low computer accessibility 6 (10) Low IT literacy among doctors 5 (8) Lack of system with good research 4 (7) Few experienced respected CDSS developers 4 (7) No time to learn new technology 2 (3) Others 4 (5)

Total 62 (1 00)

Fig 52 Reasons cited by respondents for slow adoption of CDSS

Theme No ()

Improve productivity 7 (35) Access of information for doctors 6 (30) Public health education 4 (20) Improve quality of decisions 3 (15)

Total 20 (1 00)

Fig 53 Reasons CDSS can be beneficial

if networked with patients on the Internet

Theme No ()

Insufficient medical knowledge of patients Absence of clinical evaluation 4 (18) Inaccuracy of system 2 (9) Others 4 (18)

12 (55 )

Total 22 (1 00)

Fig 54 Reasons CDSS can be detrimental

if networked with patients on the Internet

Among tangible economic benefits of CDSS would be the savings achieved by using less medical resources and time to arrive at a diagnosis as CDSS produced a tested fixed procedure to arrive at the optimum decision on a range of cases The project had shown that there were various interest groups with different needs and prospects of CDSS adoption were higher when these needs were met

6 Conclusion This project by avoiding the study of single variables

and instead focus on total situations (the technical social and organisational context) of information systems yield some useful lessons

The participative approach is essential for medical information systems development By involving users be part of the development process of CDSS they contribute to the functional requirements of the CDSS such as consultative participation representative participation and consensus participation The danger is that through the use of CDSS management may pressure the doctorsrsquo conformity to organisational goals and to reduce frequency of expensive investigational treatment

This research has highlighted the problems and hurdles facing CDSS Many new techniques and developments have emerged during the last one year It is time for researchers to explore the design and usage of flexible web-based decision support system generator utilising software agents to provide additional functionalities

Theme NO (lsquoYo)

Clinical trialsto improve accuracy 13 (28) Information about the programme pros and cons 11 (23) medical workshops and seminars about CDSS 8 (1 7) Prototype to try 4 (9) Public awareness (through mass media) 4 (9) Clear misconceptions of doctors 3 (6)

User-friendly programme 2 (4) Higher computer literacy rate 2 (4)

(CDSS replaces them)

Total 47 (1 00)

Fig 55 Ways to improve adoption of CDSS

7 Acknowledgement The authors appreciate the cooperation of numerous

medical specialists and health pesonnel who have very kindly been involved in interviews and the survey The supports of the University of Auckland (NZ) and the HELP Institute (Malaysia) in enabling this project to be conducted are gratefully acknowledged here

8 References 1 Avgerou C and Comford T 1993 Developing Information

SystemsConcepts Issues and Practise Basingstroke U K Macmillan

2 Barahona P amp Chnstensen JP 1994 ldquoKnowledge and decisions in Health Telematicsrdquo Proceedings EPISTOL Health Decision Support Netherlands 10s Press

912

3 Checkland P 1981 Systems Thinking Systems Practice Chichester UK John Wiley amp Sons

4 Checkland P and Scholes J 1990 Soft Systems Methodology in Action Chichester UK John Wiley amp Sons Ltd Classen D 1998 ldquoClinincal Decision Support Systems to Improve Clininal Practice and Quality of Carerdquo

Hunt DL Haynes RB Hanna SE Smith K ldquoEffects of computer based clinical decision support systems on physician performance and paient outcomes a systematic reviewldquo Journal of the American Medical

Loo G L and Lee 2000 ldquoAn Interface to Databases for Flexible Query Answering A FuzzySet Approachrsquo

5

JAMA Oct 21V28 N015 pp1360-61 6

ASSOC (JAMA) 1998 280 1339-1346 7

Efficacy

Efficiency

Effectivencss

Ethicality

Elegance

Joumal LNCS 1873 [ 1 Ith Int Conf UK DEXA 20001

8 Mazoue J G 1990 The Journal of Medicine and pp 654-663

Philosophy ldquoDiagnosis without doctorsrdquo Vol 15 pp 559-579

9 McNeill P 1985 Research Methods UK Routledge 10 Shortliffe E H 1987 JAMA ldquoComputer Programs to

Support Clinical Decision Makingrdquo 3 July Vol 258 No 1 1 Sprague R H 1980 ldquoA Framework for the Development of DSSrdquo MIS Quarterly

1 1 Weaver RR 199 1 Computers andMedical Knowledge The diffusion of decision support systems Colorado USA Westview Press

12 Wilson B 1984 Systems Concepts Methodologies and Applications Chichester UK John Wiley amp Sons

Able to represent knowledge base accurately using artificial intelligence

Reduce the use of medical resourccs through better diagnosis accuracy

Doctors havc more time for patient care when unneccssary mistakes are avoided

Provide option for doctors to deploy CDSS or involve them in dcvelopment

User friendly computer interface

9 Appendix

Fig Al The 5 Es to measure CDSS feasibility in this project

C Customersrsquo - doctors medical students

A lsquoActorsrsquo - hospital director and management team

T Tran sformation - need for diagnosis accuracy and efficiency6 need met via

W Weltanschauungrsquo - a belief that technology could reduce human errors

0 Ownersrsquo - government (Ministry of Health)

E Environment - relationship between doctor and patient computer literacy

processrsquo clinical decision support and learning using artificial intelligence

constraintsrsquo

Fig A2 The primary task root definition Root definition(Pictorialdiagrammatic representation of the situations$ entities(structures) proces ses relationships and isuues)

913

2 0 b s e r v e d i a g n o s i s and

4 D e f i n e its relation to C D S S g o a l s

8 D e f i n e criteria for e f f i c a c y e f f i c i e n c y e f f e c t i v e

9 Monitor 1 - 7

Fig A3 A rough model of major necessary subsystems for CDSS developmen

I Scale and item I Mean f SD I Reliability I General computer orientation 303 f 096 080 CDSS will increase the productivity of hospitals 274 f 086 CDSS creates hassles for clinical staff 291 f081 CDSS can do jobs better than people 358 f 101 In the long run CDSS decrease the hospitalk cost 294 f 093 Use of this technology is unavoidable 265 f 089 I am confident that CDSS will succeed 285 f 078 Frequent CDSS breakdown is anticipated= 392 f 073 Job security 220 f 099 Increased use of CDSS mean less work for people 264 + 094 CDSS will threaten my future where I work= 213 f 092 CDSS will reduce my job security= 213 f 085 CDSS will make me less useful as a worker 206 k 1 OO My job skills are rapidly becoming obsolete 205 f 1 I 1 Professional impact 270 f 086 CDSS will enhance opportunities for medical care 241 f 0 7 5 CDSS will free up time for more professional activities 288 f 093 CDSS will upgrade job descriptions 282 f 082 Management concern 311 k 100 Managements interest is to improve productivity 324 f 109 Without concern for employees= Management will involve people in 274 f 089 Planning for implementation When management explain CDSS plans they 347 f 102 will not tell us the whole story=

086

071

065

Fig A4 Attitudes of doctors towards CDSS analysed using SPSS programme

-Negatively worded items were reverse coded before data inalysis Scores ranged from 1 to 5 the lower the mean the more positive the response SD=standard deviation

9 14

Lack of exposure 14 (23) Insufficient funds 9 (15) System is not cost effective 7(11) Bureaucracy red tape 7 (1 1) Low computer accessibility 6 (10) Low IT literacy among doctors 5 (8) Lack of system with good research 4 (7) Few experienced respected CDSS developers 4 (7) No time to learn new technology 2 (3) Others 4 (5)

Total 62 (1 00)

Fig 52 Reasons cited by respondents for slow adoption of CDSS

Theme No ()

Improve productivity 7 (35) Access of information for doctors 6 (30) Public health education 4 (20) Improve quality of decisions 3 (15)

Total 20 (1 00)

Fig 53 Reasons CDSS can be beneficial

if networked with patients on the Internet

Theme No ()

Insufficient medical knowledge of patients Absence of clinical evaluation 4 (18) Inaccuracy of system 2 (9) Others 4 (18)

12 (55 )

Total 22 (1 00)

Fig 54 Reasons CDSS can be detrimental

if networked with patients on the Internet

Among tangible economic benefits of CDSS would be the savings achieved by using less medical resources and time to arrive at a diagnosis as CDSS produced a tested fixed procedure to arrive at the optimum decision on a range of cases The project had shown that there were various interest groups with different needs and prospects of CDSS adoption were higher when these needs were met

6 Conclusion This project by avoiding the study of single variables

and instead focus on total situations (the technical social and organisational context) of information systems yield some useful lessons

The participative approach is essential for medical information systems development By involving users be part of the development process of CDSS they contribute to the functional requirements of the CDSS such as consultative participation representative participation and consensus participation The danger is that through the use of CDSS management may pressure the doctorsrsquo conformity to organisational goals and to reduce frequency of expensive investigational treatment

This research has highlighted the problems and hurdles facing CDSS Many new techniques and developments have emerged during the last one year It is time for researchers to explore the design and usage of flexible web-based decision support system generator utilising software agents to provide additional functionalities

Theme NO (lsquoYo)

Clinical trialsto improve accuracy 13 (28) Information about the programme pros and cons 11 (23) medical workshops and seminars about CDSS 8 (1 7) Prototype to try 4 (9) Public awareness (through mass media) 4 (9) Clear misconceptions of doctors 3 (6)

User-friendly programme 2 (4) Higher computer literacy rate 2 (4)

(CDSS replaces them)

Total 47 (1 00)

Fig 55 Ways to improve adoption of CDSS

7 Acknowledgement The authors appreciate the cooperation of numerous

medical specialists and health pesonnel who have very kindly been involved in interviews and the survey The supports of the University of Auckland (NZ) and the HELP Institute (Malaysia) in enabling this project to be conducted are gratefully acknowledged here

8 References 1 Avgerou C and Comford T 1993 Developing Information

SystemsConcepts Issues and Practise Basingstroke U K Macmillan

2 Barahona P amp Chnstensen JP 1994 ldquoKnowledge and decisions in Health Telematicsrdquo Proceedings EPISTOL Health Decision Support Netherlands 10s Press

912

3 Checkland P 1981 Systems Thinking Systems Practice Chichester UK John Wiley amp Sons

4 Checkland P and Scholes J 1990 Soft Systems Methodology in Action Chichester UK John Wiley amp Sons Ltd Classen D 1998 ldquoClinincal Decision Support Systems to Improve Clininal Practice and Quality of Carerdquo

Hunt DL Haynes RB Hanna SE Smith K ldquoEffects of computer based clinical decision support systems on physician performance and paient outcomes a systematic reviewldquo Journal of the American Medical

Loo G L and Lee 2000 ldquoAn Interface to Databases for Flexible Query Answering A FuzzySet Approachrsquo

5

JAMA Oct 21V28 N015 pp1360-61 6

ASSOC (JAMA) 1998 280 1339-1346 7

Efficacy

Efficiency

Effectivencss

Ethicality

Elegance

Joumal LNCS 1873 [ 1 Ith Int Conf UK DEXA 20001

8 Mazoue J G 1990 The Journal of Medicine and pp 654-663

Philosophy ldquoDiagnosis without doctorsrdquo Vol 15 pp 559-579

9 McNeill P 1985 Research Methods UK Routledge 10 Shortliffe E H 1987 JAMA ldquoComputer Programs to

Support Clinical Decision Makingrdquo 3 July Vol 258 No 1 1 Sprague R H 1980 ldquoA Framework for the Development of DSSrdquo MIS Quarterly

1 1 Weaver RR 199 1 Computers andMedical Knowledge The diffusion of decision support systems Colorado USA Westview Press

12 Wilson B 1984 Systems Concepts Methodologies and Applications Chichester UK John Wiley amp Sons

Able to represent knowledge base accurately using artificial intelligence

Reduce the use of medical resourccs through better diagnosis accuracy

Doctors havc more time for patient care when unneccssary mistakes are avoided

Provide option for doctors to deploy CDSS or involve them in dcvelopment

User friendly computer interface

9 Appendix

Fig Al The 5 Es to measure CDSS feasibility in this project

C Customersrsquo - doctors medical students

A lsquoActorsrsquo - hospital director and management team

T Tran sformation - need for diagnosis accuracy and efficiency6 need met via

W Weltanschauungrsquo - a belief that technology could reduce human errors

0 Ownersrsquo - government (Ministry of Health)

E Environment - relationship between doctor and patient computer literacy

processrsquo clinical decision support and learning using artificial intelligence

constraintsrsquo

Fig A2 The primary task root definition Root definition(Pictorialdiagrammatic representation of the situations$ entities(structures) proces ses relationships and isuues)

913

2 0 b s e r v e d i a g n o s i s and

4 D e f i n e its relation to C D S S g o a l s

8 D e f i n e criteria for e f f i c a c y e f f i c i e n c y e f f e c t i v e

9 Monitor 1 - 7

Fig A3 A rough model of major necessary subsystems for CDSS developmen

I Scale and item I Mean f SD I Reliability I General computer orientation 303 f 096 080 CDSS will increase the productivity of hospitals 274 f 086 CDSS creates hassles for clinical staff 291 f081 CDSS can do jobs better than people 358 f 101 In the long run CDSS decrease the hospitalk cost 294 f 093 Use of this technology is unavoidable 265 f 089 I am confident that CDSS will succeed 285 f 078 Frequent CDSS breakdown is anticipated= 392 f 073 Job security 220 f 099 Increased use of CDSS mean less work for people 264 + 094 CDSS will threaten my future where I work= 213 f 092 CDSS will reduce my job security= 213 f 085 CDSS will make me less useful as a worker 206 k 1 OO My job skills are rapidly becoming obsolete 205 f 1 I 1 Professional impact 270 f 086 CDSS will enhance opportunities for medical care 241 f 0 7 5 CDSS will free up time for more professional activities 288 f 093 CDSS will upgrade job descriptions 282 f 082 Management concern 311 k 100 Managements interest is to improve productivity 324 f 109 Without concern for employees= Management will involve people in 274 f 089 Planning for implementation When management explain CDSS plans they 347 f 102 will not tell us the whole story=

086

071

065

Fig A4 Attitudes of doctors towards CDSS analysed using SPSS programme

-Negatively worded items were reverse coded before data inalysis Scores ranged from 1 to 5 the lower the mean the more positive the response SD=standard deviation

9 14

3 Checkland P 1981 Systems Thinking Systems Practice Chichester UK John Wiley amp Sons

4 Checkland P and Scholes J 1990 Soft Systems Methodology in Action Chichester UK John Wiley amp Sons Ltd Classen D 1998 ldquoClinincal Decision Support Systems to Improve Clininal Practice and Quality of Carerdquo

Hunt DL Haynes RB Hanna SE Smith K ldquoEffects of computer based clinical decision support systems on physician performance and paient outcomes a systematic reviewldquo Journal of the American Medical

Loo G L and Lee 2000 ldquoAn Interface to Databases for Flexible Query Answering A FuzzySet Approachrsquo

5

JAMA Oct 21V28 N015 pp1360-61 6

ASSOC (JAMA) 1998 280 1339-1346 7

Efficacy

Efficiency

Effectivencss

Ethicality

Elegance

Joumal LNCS 1873 [ 1 Ith Int Conf UK DEXA 20001

8 Mazoue J G 1990 The Journal of Medicine and pp 654-663

Philosophy ldquoDiagnosis without doctorsrdquo Vol 15 pp 559-579

9 McNeill P 1985 Research Methods UK Routledge 10 Shortliffe E H 1987 JAMA ldquoComputer Programs to

Support Clinical Decision Makingrdquo 3 July Vol 258 No 1 1 Sprague R H 1980 ldquoA Framework for the Development of DSSrdquo MIS Quarterly

1 1 Weaver RR 199 1 Computers andMedical Knowledge The diffusion of decision support systems Colorado USA Westview Press

12 Wilson B 1984 Systems Concepts Methodologies and Applications Chichester UK John Wiley amp Sons

Able to represent knowledge base accurately using artificial intelligence

Reduce the use of medical resourccs through better diagnosis accuracy

Doctors havc more time for patient care when unneccssary mistakes are avoided

Provide option for doctors to deploy CDSS or involve them in dcvelopment

User friendly computer interface

9 Appendix

Fig Al The 5 Es to measure CDSS feasibility in this project

C Customersrsquo - doctors medical students

A lsquoActorsrsquo - hospital director and management team

T Tran sformation - need for diagnosis accuracy and efficiency6 need met via

W Weltanschauungrsquo - a belief that technology could reduce human errors

0 Ownersrsquo - government (Ministry of Health)

E Environment - relationship between doctor and patient computer literacy

processrsquo clinical decision support and learning using artificial intelligence

constraintsrsquo

Fig A2 The primary task root definition Root definition(Pictorialdiagrammatic representation of the situations$ entities(structures) proces ses relationships and isuues)

913

2 0 b s e r v e d i a g n o s i s and

4 D e f i n e its relation to C D S S g o a l s

8 D e f i n e criteria for e f f i c a c y e f f i c i e n c y e f f e c t i v e

9 Monitor 1 - 7

Fig A3 A rough model of major necessary subsystems for CDSS developmen

I Scale and item I Mean f SD I Reliability I General computer orientation 303 f 096 080 CDSS will increase the productivity of hospitals 274 f 086 CDSS creates hassles for clinical staff 291 f081 CDSS can do jobs better than people 358 f 101 In the long run CDSS decrease the hospitalk cost 294 f 093 Use of this technology is unavoidable 265 f 089 I am confident that CDSS will succeed 285 f 078 Frequent CDSS breakdown is anticipated= 392 f 073 Job security 220 f 099 Increased use of CDSS mean less work for people 264 + 094 CDSS will threaten my future where I work= 213 f 092 CDSS will reduce my job security= 213 f 085 CDSS will make me less useful as a worker 206 k 1 OO My job skills are rapidly becoming obsolete 205 f 1 I 1 Professional impact 270 f 086 CDSS will enhance opportunities for medical care 241 f 0 7 5 CDSS will free up time for more professional activities 288 f 093 CDSS will upgrade job descriptions 282 f 082 Management concern 311 k 100 Managements interest is to improve productivity 324 f 109 Without concern for employees= Management will involve people in 274 f 089 Planning for implementation When management explain CDSS plans they 347 f 102 will not tell us the whole story=

086

071

065

Fig A4 Attitudes of doctors towards CDSS analysed using SPSS programme

-Negatively worded items were reverse coded before data inalysis Scores ranged from 1 to 5 the lower the mean the more positive the response SD=standard deviation

9 14

2 0 b s e r v e d i a g n o s i s and

4 D e f i n e its relation to C D S S g o a l s

8 D e f i n e criteria for e f f i c a c y e f f i c i e n c y e f f e c t i v e

9 Monitor 1 - 7

Fig A3 A rough model of major necessary subsystems for CDSS developmen

I Scale and item I Mean f SD I Reliability I General computer orientation 303 f 096 080 CDSS will increase the productivity of hospitals 274 f 086 CDSS creates hassles for clinical staff 291 f081 CDSS can do jobs better than people 358 f 101 In the long run CDSS decrease the hospitalk cost 294 f 093 Use of this technology is unavoidable 265 f 089 I am confident that CDSS will succeed 285 f 078 Frequent CDSS breakdown is anticipated= 392 f 073 Job security 220 f 099 Increased use of CDSS mean less work for people 264 + 094 CDSS will threaten my future where I work= 213 f 092 CDSS will reduce my job security= 213 f 085 CDSS will make me less useful as a worker 206 k 1 OO My job skills are rapidly becoming obsolete 205 f 1 I 1 Professional impact 270 f 086 CDSS will enhance opportunities for medical care 241 f 0 7 5 CDSS will free up time for more professional activities 288 f 093 CDSS will upgrade job descriptions 282 f 082 Management concern 311 k 100 Managements interest is to improve productivity 324 f 109 Without concern for employees= Management will involve people in 274 f 089 Planning for implementation When management explain CDSS plans they 347 f 102 will not tell us the whole story=

086

071

065

Fig A4 Attitudes of doctors towards CDSS analysed using SPSS programme

-Negatively worded items were reverse coded before data inalysis Scores ranged from 1 to 5 the lower the mean the more positive the response SD=standard deviation

9 14