Document Type : Original Article
Authors
1 Department of Health Information Technology and Management, Social Determinant of Health Research Center
2 Department of Neurology, School of Medicine, Isfahan Research Committee on Multiple Sclerosis, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract
Context: Establishing and developing minimum data set (MDS), controlled vocabularies,
taxonomies and classification systems are requirements of health information system in every
society. Aims: The aim of this study was to propose an integrated multiple sclerosis (MS) data
set by comparing European database for multiple sclerosis (EDMUS Coordinating Center Lyon,
France) and iMed© software’s (iMed, Merck Serono SA - Geneva). EDMUS is being developed at
the EDMUS coordinating centers in Lyon, France and iMed© is owned and distributed by Merck
Serono in Geneva, Switzerland. Settings and Designs: Retrieval of data of MDS performed
through scholars responsible in related agencies and clinics. Materials and Methods: This
research was an applied. The study was comparative-exploratory. In this study, data elements
in iMed© and EDMUS software’s were compared. Data collecting tool was data raw form.
Statistical Analysis Used: Results analyzing was carried out in a descriptive-comparative
method. MS data elements were proposed in three general categories: administrative; clinical;
and socio-economic. In this study, a MS data set was suggested by studying data elements of
EDMUS and iMed© softwares. Results: The MS data set includes administrative, clinical and
socio-economic data elements that collect information of MS patients during the treatment
course. iMed©, EDMUS and other available databases are suitable patterns for determining
and recognizing MS key data elements. Conclusion: Developing MS data set in this study
and studying other available MS information systems result in establishing standardized MS
data set. By establishing this data set, it will be possible to present MS MDS internationally.
MS MDS is the main base of establishing MS information systems at different levels.
Keywords
- Young JJ. Handbook for Brunner and Suddarth’s Textbook of
Medical‑Surgical Nursing. Tehran: Andisheye Rafi; 2004.
2. Etemadifar M, Janghorbani M, Shaygannejad V, Ashtari F.
Prevalence of multiple sclerosis in Isfahan, Iran. Neuroepidemiology
2006;27:39‑44.
3. About MS. Multiple sclerosis international federation site. Available
from: http://www.msif.org/en/about_ms/index.html. [Last Accessed
on Aug 23, 2011]. - 4. Etemadifar M, Maghzi AH. Sharp increase in the incidence and
prevalence of multiple sclerosis in Isfahan, Iran. Mult Scler
2011;17:1022‑7.
5. Etemadifar M, Abtahi SH. Multiple sclerosis in Isfahan, Iran: Past,
present and future. Int J Prev Med 2012;3 (5):301‑2.
6. Saadatnia M, Etemadifar M, Maghzi AH. Multiple sclerosis in Isfahan,
Iran. Int Rev Neurobiol 2007;79:357‑75. ISSN: 0074‑7742, ISBN:
978012373766.
7. Flachenecker P, Khil L, Bergmann S, Kowalewski M, Pascu I,
Pérez‑Miralles F, et al. Development and pilot phase of a European
MS register. J Neurol 2010;257:1620‑7.
8. Jahanbakhsh M. A Comparative Study for Hospital‑based Diabetes
Registry in the Selected Countries and Designing a Model for
Iran [Thesis in M.Sc.]. Tehran: Shahid Beheshti University of Medical
Sciences and Health Services; 2005.
9. Ajami S. A Comparative study on Earthquake Information
Management Systems (EIMS) in India, Afghanistan and Iran.
Journal of Education and Health Promotion. 2012;1:27‑34. DOI:
10.4103/2277‑9531.99963.
10. Confavreux C, Paty DW. Current status of computerization of
multiple sclerosis clinical data for research in Europe and North
America: The EDMUS/MS‑COSTAR connection. European Database
for Multiple Sclerosis. Multiple Sclerosis‑Computed Stored
Ambulatory Record. Neurology 1995;45 (3 Pt 1):573‑6.
11. Ajami S, Ketabi S. Performance evaluation of medical records
departments by analytical hierarchy process (AHP) approach in the
selected hospitals in Isfahan: Medical records dep. and AHP. J Med
Syst 2012;36:1165‑71.
12. Ajami S, Ketabi S, Isfahani SS, Heidari A. Readiness assessment
of electronic health records implementation. Acta Inform Med
2011;19:224‑7.
13. Rajab AA. A methodology for developing a nursing education
minimum dataset [Thesis]. University of South Florida in USA; 2005.
Available from: http://scholarcommons.usf.edu/cgi/viewcontent.
cgi?article=1825&context=etd. [Last Accessed on 2013 Jul 10].
14. Ajami S, Bagheri‑Tadi T. Barriers for adopting electronic health
records (EHRs) by physicians. Acta Inform Med 2013;21:129‑34.
15. Karimi S, Saghaeian‑Nejad‑Isfahani S, Farzandipour M,
Esmaeli‑Ghayoumabadi M. Comparative study of minimum data
sets of health information management of organ transplantation
in selected countries and presenting appropriate solution for Iran.
Health Information Management 2011;7 (S):497‑505.
16. Keyvanara M, Sadeghi M, Saghaeiannejad‑Isfahani S, Tadayyon H.
A comparative review of national registry systems of acute coronary
syndrome in selective countries. Health Inf Manage 2012;9:172‑9.
17. Carter J, Evans J, Tuttle M, Weida T, White T, Harvell J, et al.
Making the Minimum Data Set Compliant with Health Information
Technology Standards. Washington, D.C.: ASPE/DALTCP and
Aplelon, Inc; 2006.
18. Fard‑Azar FE, Tofighi S, Bashardost N, Ajami S. A comparative
survey on mortality information management systems in England,
United States of America and New Zealand and proposing a suitable
MIMS model for Iran. J Qazvin Univ Med Sci 2004;8:81‑8.
19. Tadayon H. Comparative Study of National Registry of Acute
Coronary Syndrome in Selected Countries and Presenting
Appropriate Guidelines for Iran. Isfahan: Isfahan University of
Medical Sciences and Health Services; 2010.
20. Trojano M. Can data basing optimize patient care? J Neurol
2004;251(S5):79‑82.
21. Devonshire V. Clinical databases in MS: Patient management and
research. Int MS J 2001;8:57‑66.
22. Confavreux C, Compston DA, Hommes OR, McDonald WI,
Thompson AJ. EDMUS, a European database for multiple sclerosis.
J Neurol Neurosurg Psychiatry 1992;55:671‑6.
23. Butzkueven H, Chapman J, Cristiano E, Grand’Maison F,
Hoffmann M, Izquierdo G, et al. MSBase: An international, online
registry and platform for collaborative outcomes research in multiple
sclerosis. Mult Scler 2006;12:769‑74.
24. What is MSBase. MSBase registry website. Available from: https://
www.msbase.org. [Last cited on 2013 Jul 23].
25. Flachenecker P, Stuke K. National MS registries. J Neurol
2008;255(S6):102‑8.
26. Trojano M, Paolicelli D, Lepore V, Fuiani A, Di Monte E, Pellegrini F,
et al. Italian Multiple Sclerosis Database Network. Neurol Sci
2006;27(S5): 358‑61.
27. About iMed. Merck Serono, Geneva. iMed Site. Available from: http://
www.imed.org/en/index.html. [Last Accessed on July 24, 2013].
28. Merck Serono SA. iMed: Electronic Multiple Sclerosis Patient Clinical
Database. User Manual. Version 6. Geneva, Switzerland: Merck
Serono Company; 2012.
29. The EDMUS Project. Available from: http://www.edmus.org/en/
proj/index.html.[Last Accessed on Aug 12, 2013].
30. The MSBase Foundation Ltd. MSBase: Free International Online
Registry for MS Researchers (MSBase Brochure). Available from:
https://www.msbase.org/msbase/cms: contentasbinary/documents/
MSBase_Brochure.pdf. [Last Accessed on Aug 12, 2013].
31. Hurwitz BJ. Registry studies of long‑term multiple sclerosis
outcomes: Description of key registries. Neurology 2011;76(S3):6.
32. Free Trial Version of EDMUS 5.0 Software. Available from: http://
www.edmus.org/en/soft/edmus_get.html. [Last Accessed on 2013
Sep 23].
33. iMed Version 5.4.5 Software. Iranian Merk Serono Representative.
Tehran 2012. Available from: http://www.merckserono.com/en/
index.html. [LastAccessed on 2013 Aug 12].
34. NINDS CDE Team Project: Multiple sclerosis. NINDS common data
elements site. Available from: http://www.commondataelements.
ninds.nih.gov/MS.aspx#tab=Data_Standards. [Last Accessed on
July 10, 2013].
35. Unertl KM, Weinger MB, Johnson KB. Applying direct observation
to model workflow and assess adoption. AMIA Annu Symp Proc
2006;2006:794‑8.
36. Palace J, Boggild M. The UK multiple sclerosis database. Mult Scler
1999;5:297‑8.
37. Callaly T, Faulkner P, Hollis G, McIlroy D, Hantz P. The development
of a mental health service patient information management system.
Aust Health Rev 1998;21:182‑93.
38. Treviño FM. Uniform minimum data sets: In search of demographic
comparability. Am J Public Health 1988;78:126‑7.
39. Safdari R, Akbari M, Tofighi S, Moeinolghorabaii M, Karami G.
Comparative study of clinical information systems of mental
illness caused by the war in America, England and Australia
and Offer Appropriate Solutions for Iran. Res J Med Veteran
2009;3:44‑9.
40. Mehrdad R. Health system in Iran. JMAJ 2009;52:69‑73.
41. Weinshenker BG. Databases in MS research: Pitfalls and promises.
Mult Scler 1999;5:206‑11.
42. Mittman R. Using Clinical Information Technology in Chronic Disease
Care: Exper Workshop Summary: California HealthCare Foundation;
2004.
43. National Institute of Biomedical Imaging and Bioengineering/
National Heart, Lung, and Blood Institute/National Science
Foundation Workshop Faculty, Price CP, Kricka LJ. Improving
healthcare accessibility through point‑of‑care technologies. Clin
Chem 2007;53:1665‑75.
44. Sahay S, Monteiro E, Aanestad M, editors. Towards a Political
Perspective of Integrative Research: The Case of Health Information
Systems in India. The 9th International Conference on Social
Implications of Computers in Developing Countries; 2007; São
Paulo, Brazil.
45. Laing K. Use of the SGNA minimum data set in the clinical area.
Gastroenterol Nurs 2005;28:59‑60.
46. Buchanan RJ, Wang S, Huang C, Graber D. Profiles of nursing home
residents with multiple sclerosis using the minimum data set. Mult
Scler 2001;7:189‑200.
47. Torabi M, Safdari R, Shahmoradi L. Health Information Technology
Management. Tehran: Jafari; 2010.
48. Davis N, Lacour M. Introduction to Health Information Technology.
1st ed. USA: W.B. Saunders Company; 2002. - 49. Hosseini A, Moghaddasi H, Jahanbakhsh M. Designing minimum
data sets of diabetes mellitus: Basis of effectiveness indicators
of diabetes management. Health Information Management
2010;7:330‑40.
50. Bandari DS, Vollmer TL, Khatri BO, Tyry T. Assessing quality of life
in patients with multiple sclerosis. Int J MS Care 2010;12:34‑41.
51. Johns M. Health Information Management Technology: An
Applied Approach. Chicago: American Health Information
Management (AHIMA); 2002.