Business Process Modelling is an Essential Part of a Requirements Analysis

Summary

Objectives

To perform a requirements analysis of the barriers to conducting research linking of primary care, genetic and cancer data.

Methods

We extended our initial data-centric approach to include socio-culturalandbusinessrequirements.Wecreatedreferencemodels of core data requirements common to most studies using unified modelling language (UML), dataflow diagrams (DFD) and business process modelling notation (BPMN). We conducted a stakeholder analysis and constructed DFD and UML diagrams for use cases based on simulated research studies. We used research output as a sensitivity analysis.

Results

Differences between the reference model and use cases identified study specific data requirements. The stakeholder analysis identified: tensions, changes in specification, some indifference from data providers and enthusiastic informaticians urging inclusion of socio-cultural context. We identified requirements to collect information at three levels: microdata items, which need to be semantically interoperable, meso-the medical record and data extraction, and macro-the health system and socio-cultural issues. BPMN clarified complex business requirements among data providers and vendors; and additional geographical requirements for patients to be represented in both linked datasets. High quality research output was the norm for most repositories.

Conclusions

Reference models provide high-level schemata of the core data requirements. However, business requirements’ modelling identifies stakeholder issues and identifies what needs to be addressed to enable participation.

 

MissingLinkText

Keywords

General practice - medical records systems - computerized - research design - registry - records as topic

 

 

MissingLinkText

• References

• 1 Cadle J, Paul D, Turner P. Business analysis techniques, 72 Essential Tools for Success. Swindon: BCS; 2010
• 2 Alexander I, Stevens R. Writing better requirements. London: Addison-Wesley; 2002
• 3 Krause P, de Lusignan S. Procuring interoperability at the expense of usability: a case study of UK National Programme for IT assurance process. Stud Health Technol Inform 2010; 155: 143-9.
• 4 Hood C, Wiedemann P, Fichtinger S, Pautz U. The interface between requirements development and all other systems engineering processes. Berlin: Springer-Verlag; 2010
• 5 Reddy M, Pratt W, Dourish P, Shabot M. Sociotechnical requirements analysis for clinical systems. Methods Inf Med 2003; 42 (04) 437-44.
• 6 Montabert C, McCrickard D, Winchester W, Perez-Quinones M. An integrative approach to requirements analysis: How task models support requirements reuse in a user-centric design framework. Interact Comput Aug 2009; 21 (04) 304-15 DOI: 10.1016/j.intcom.2009.06.003.
• 7 Surendra N. Using an ethnographic process to conduct requirements analysis for agile systems development. Inf Technol Manag 2008; 09 (01) 55-68 DOI 10.1007/s10799-007-0026-6.
• 8 Matteson S, Paulauskis J, Foisy S, Hall S, Duval M. Opening the gate for genomics data into clinical research: a use case in managing patients’ DNA samples from the bench to drug development. Pharmacogenomics 2010; 11 (11) 1603-12.
• 9 International Human Genome Sequencing Consortium.Initial sequencing and analysis of the human genome. Nature 2001; 409 (6822): 860-921.
• 10 International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004; 431 (7011): 931-45.
• 11 Hirtzlin I, Dubreuil C, Préaubert N, Duchier J, Jansen B, Simon J. EUROGENBANK Consortium. et al. An empirical survey on biobanking of human genetic material and data in six EU countries. Eur J Hum Genet 2003; 11 (06) 475-88.
• 12 McEwen J. Forensic DNA data banking by state crime laboratories. Am J Hum Genet 1995; 56 (06) 1487-92.
• 13 Schneider B. Progress in cancer control through cancer registries. CA Cancer J Clin 1958; 08 (06) 207-10.
• 14 Navarro C, Martos C, Ardanaz E, Galceran J, Izarzugaza I, Peris-Bonet R. et al. Spanish Cancer Registries Working Group. Population-based cancer registries in Spain and their role in cancer control. Ann Oncol 2010; 21 Suppl (Suppl. 03) iii3-13.
• 15 Gatta G, Capocaccia R, Trama A, Martínez-García C. RARECARE Working Group. The burden of rare cancers in Europe. Adv Exp Med Biol 2010; 686: 285-303.
• 16 Francisci S, Capocaccia R, Grande E, Santaquilani M, Simonetti A, Allemani C. et al. EUROCARE Working Group. The cure of cancer: a European perspective. Eur J Cancer 2009; Apr; 45 (06) 1067-79.
• 17 de Lusignan S, Chan T. The development of primary care information technology in the United Kingdom. J Ambul Care Manag 2008; Jul-Sep; 31 (03) 201-10.
• 18 de Lusignan S, van Weel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Fam Pract 2006; 23 (02) 253-63.
• 19 Clement S, Pickering A, Rowlands G, Thiru K, Candy B, de Lusignan S. Towards a conceptual framework for evaluating primary care research networks. Br J Gen Pract 2000; 50 (457) 651-2.
• 20 Peterson KA, Fontaine P, Speedie S. The Electronic Primary Care Research Network (ePCRN): a new era in practice-based research. J Am Board Fam Med 2006; 19 (01) 93-7.
• 21 Boffin N, Bossuyt N, Vanthomme K, Van Casteren V. Readiness of the Belgian network of sentinel general practitioners to deliver electronic health record data for surveillance purposes: results of survey study. BMC Fam Pract 2010; 11 (01) 50.
• 22 Friedman C, Hripcsak G, Johnson SB, Cimino JJ, Clayton PD. A generalized relational schema for an integrated clinical patient database. Proceedings 14th Annual Symposium on Computer Applications in Medical Care. Los Alamitos, CA: IEEE Computer Society Press; 1990: 335-9.
• 23 Prokosch HU, Ganslandt T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf Med 2009; 48: 38-44.
• 24 Burgun A, Bodenreider O. Accessing and integrating data and knowledge for biomedical research. Yearb Med Inform 2008; 91-101.
• 25 Ohmann C, Kuchinke W. Future developments of medical informatics from the viewpoint of networked clinical research. Interoperability and integration. Methods Inf Med 2009; 48 (01) 45-54.
• 26 Mirhaji P, Zhu M, Vagnoni M, Bernstam EV, Zhang J, Smith JW. Ontology driven integration platform for clinical and translational research. BMC Bioinformatics 2009; 10 Suppl (Suppl. 02) S2.
• 27 Wang X, Liu L, Fackenthal J, Cummings S, Olopade OI, Hope K. et al. Translational integrity and continuity: personalized biomedical data integration. J Biomed Inform 2009; 42 (01) 100-12.
• 28 Huo M, Verner J, Zhu L, Ali MBarbar. Software quality and agile methods. Proceedings of 28th Annual International Computer Software and Applications Conference (COMPSAC’04) 2004; 01: 520-5 http://doi.ieeecomputersociety. org/10.1109/CMPSAC.2004.1342889
• 29 de Lusignan S, Peace C, Shaw N, Liaw S-T, Michalakeidis G, Vincente M. et al. What are the barriers to conducting International research using routinely collected primary care data?. Stud Health Technol Inform 2011; 165: 135-40 DOI: 10.3233/ 978-1-60750-735-2-135.
• 30 de Lusignan S, Liaw S-T, Krause P, Curcin V, Vincente M, Michalakidis G. et al. Key concepts to assess the readiness of data for International research: Data quality, lineage and provenance, extraction and processing errors, traceability, and curation. Yearb Med Inform 2011; 06 (01) 112-20.
• 31 TRANSFoRm (Translational Research and Patients safety In Europe). URL. http:// www.transformproject.eu
• 32 European Union, Europe’s Information Society. Communication from the Commission to the Council, the European Parliament, the European Economic and Social Committee and the Committee of the Regions -e-Health -making healthcare better for European citizens: an action plan for a European e-Health Area {SEC(2004)539}. 2004 URL: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52004DC0356:EN:NOT
• 33 Kumarapeli P, de Lusignan S, Ellis T, Jones B. Using Unified Modelling Language (UML) as a process-modelling technique for clinical-research process improvement. Med Inform Internet Med 2007; Mar; 32 (01) 51-64.
• 34 de Lusignan S, Chan T, Theadom A, Dhoul N. The roles of policy and professionalism in the protection of processed clinical data: a literature review. Int J Med Inform 2007; Apr; 76 (04) 261-8.
• 35 Blobel B. Ontologies, knowledge representation, artificial intelligence – hype or prerequisites for International pHealth interoperability. Stud Health Technol Inform 2011; 165: 11-20 DOI: 10.3233/ 978-1-60750-735-2-11.
• 36 van Vlymen J, de Lusignan S, Hague N, Chan T, Dzregah B. Ensuring the quality of aggregated general practice data: lessons from the Primary Care Data Quality Programme (PCDQ). Stud Health Technol Inform 2005; 116: 1010-15.
• 37 van Vlymen J, de Lusignan S. A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data. Inform Prim Care 2005; 13 (04) 281-91.
• 38 Podeswa H. UML™ for the IT Business Analyst: A Practical Guide to Object-Oriented Requirements Gathering. Boston, MA: Thompson Course technology PTR; 2005
• 39 Fakhroutdinov K. UML, Use Case Diagrams. URL: http://www.uml-diagrams.org/use-case-diagrams.html
• 40 Ambler S. UML2 Use Case Diagrams. URL: http://www.agilemodeling.com/ar tif acts/ useCaseDiagram.htm
• 41 Object Management Group (OMG) business management initiative. Business process modelling notation (BPMN), version 2. URL: http://www.bpmn.org/
• 42 Dolin RH, Alschuler L. Approaching semantic interoperability in Health Level Seven. J Am Med Inform Assoc 2011; Jan 1; 18 (01) 99-103.
• 43 Michalakidis G, Kumarapeli P, Ring A, van Vlymen J, Krause P, de Lusignan S. A system for solution-orientated reporting of errors associated with the extraction of routinely collected clinical data for research. Stu Health Technol Inform 2010; 160: 724-8.
• 44 Nadkarni PM, Brandt C. Data Extraction and Ad Hoc Query of an Entity-Attribute-Value Database. J Am Med Inform Assoc 1998; 05: 511-7.
• 45 Santos MR, Bax MP, Kalra D. Building a logical EHR architecture based on ISO 13606 standard and semantic web technologies. Stud Health Technol Inform 2010; 160 (Pt 1): 161-5.
• 46 Castano S, De Antonellis V. Global viewing of heterogeneous data sources. IEEE Transactions on Knowledge and Data Engineering 2001; 13 (02) 277-97 DOI: http://dx.doi.org/ 10.1109/69.917566.
• 47 London JW, Smalley KJ, Conner K, Smith JB. The automation of clinical trial serious adverse event reporting workflow. Clin Trials 2009; 06 (05) 446-54.
• 48 Estrella F, Hauer T, McClatchey R, Odeh M, Rogulin D, Solomonides T. Experiences of engineering Grid-based medical software. Int J Med Inform 2007; 76 (08) 621-32.
• 49 Al-Hroub Y, Kossmann M, Odeh M. Developing an Ontology-driven Requirements Analysis Tool (OntoRAT): A Use-case-driven Approach. Proceedings of second international conference on the applications of digital information and web technologies (ICADIWT 2009) 2009; 130-8.
• 50 Weber R, Knaup P, Knietitg R, Haux R, Merzweiler A, Mludek V. et al. Object-oriented business process analysis of the cooperative soft tissue sarcoma trial of the german society for paediatric oncology and haematology (GPOH). Stud Health Technol Inform 2001; 84 (Pt 1): 58-62.
• 51 Baksi D. Model checking of healthcare domain models. Comput Methods Programs Biomed 2009; 96 (03) 217-25.
• 52 Jun GT, Ward J, Morris Z, Clarkson J. Health care process modelling: which method when?. Int J Qual Health Care 2009; 21 (03) 214-24.
• 53 Krol M, Reich DL. Object-oriented analysis and design of a health care management information system. J Med Syst 1999; 23 (02) 145-58.
• 54 Benson T. Prevention of errors and user alienation in healthcare IT integration programmes. Inform Prim Care 2007; 15 (01) 1-7.
• 55 Kumarapeli P, De Lusignan S, Ellis T, Jones B. Using Unified Modelling Language (UML) as a process-modelling technique for clinical-research process improvement. Med Inform Internet Med 2007; 32 (01) 51-64.
• 56 Vasilakis C, Lecnzarowicz D, Lee C. Application of Unified Modelling Language (UML) to the Modelling of Health Care Systems: An Introduction and Literature Survey. In Ed. Tan J. Developments in Health Information Systems and Technologies: Models and methods. Vancouver, BC: IGI-global; 2010: 275-87 DOI: 10.4018/978-1-61692-002-9.ch019.
• 57 Scheuerlein H, Rauchfuss F, Dittmar Y, Molle R, Lehmann T, Pienkos N. et al. New methods for clinical pathways-Business Process Modeling Notation (BPMN) and Tangible Business Process Modeling (t.BPM). Langenbecks Arch Surg. 2012 Feb 24. [Epub ahead of print]
• 58 Rojo MG, Daniel C, Schrader T. Standardization efforts of digital pathology in Europe. Anal Cell Pathol (Amst) 2012; 35 (01) 19-23.
• 59 Becnel LBoyd, Hunicke-Smith SP, Stafford GA, Freund ET, Ehlman M, Chandran U. et al. The caBIG(R) Life Science Business Architecture Model. Bioinformatics. 2011 Mar
• 60 Yan Q. Bioinformatics for transporter pharmaco-genomics and systems biology: data integration and modeling with UML. Methods Mol Biol 2010; 637: 23-45.
• 61 Baker EJ, Jay JJ, Philip VM, Zhang Y, Li Z, Kirova R. et al. Ontological Discovery Environment: a system for integrating gene-phenotype associations. Genomics 2009; 94 (06) 377-87.
• 62 Zheng Y, Zhou J, Krause P. An Automatic Test Case Generation Framework for Web Services. Journal of Software 2007; 02 (03) 64-77 http:// citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.94.6186&rep=rep1&type=pdf.
• 63 Tan W, Missier P, Foster I, Madduri R, Goble C. A Comparison of Using Taverna and BPEL in Building Scientific Workflows: the case of caGrid. Concurr Comput 2010; 22 (09) 1098-1117.