Identifying Data Requirements and Classification for Data Elements of Osteoporosis Progress Notes

Yadanghi, S; Kazemnejad, A; Asosheh, A

Identifying Data Requirements and Classification for Data Elements of Osteoporosis Progress Notes

Authors

S Yadanghi ¹

A Kazemnejad ²

A Asosheh ³

¹ Master of Science in Medical Informatics, Tarbiat Modares University, Faculty of Medical Sciences

² Professor of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

³ Assistant Professor, Department of Medical Informatics, Tarbiat Modares University, Faculty of Medical Sciences

Abstract

Introduction & Objective: Osteoporosis is a hidden disease that has heavy burden to patient and society. The first step in managing chronic disease is documentation and properly register disease data. This study was conducted by the goal of identifying Data requirements and introducing a structured classification for osteoporosis progress note’s dataset. Materials & Methods: This is a practical study that conducted in 2019. In this study, data elements were determined at first through the review of articles and studies, then through a questionnaire to the expert group. The results of questionnaires were analyzed by the Item Impact Index formula. Classification of data elements has been provided by studying methods of data classifications and then interviews with experts. Results: Osteoporosis progress note’s data set was presented in 9 sections and 51 items. We use HOAP – one of the SOAP adaptations – as a reference framework classification of the dataset. Conclusions: Due to the importance of registering progress notes of osteoporosis and in order to structuring these reports to identify medical procedures during patient encounter, the data elements and the classification for that were provided and evaluated.

Keywords

Osteoporosis

Progress Note

Template

HOAP

1. Sawyer A. Bone densitometry in growing patients: Springer Science & Business Media; 2007.

2. Larijani MA, Tehrani MM, Hamidi Z, Soltani A, Pajouhi M. Osteoporosis, diagnosis, prevention and treatment. Journal of Repruduction and Infertility. 2004; 6(1).

3. Fallah Mehrabadi E, Pakgohar M, Asadi S, Haghani H. Lifestyle of Elderly People With Osteoporosis and Its Related Factors. Salmand: Iranian Journal of Ageing. 2017; 12(2): 132-145.

4. Jordan KM, Cooper C. Epidemiology of osteoporosis. Best Pract Res Clin Rheumatol. 2002/12/2002; 16(5): 795-806.

5. Christodoulou C, Cooper C. What is osteoporosis? Postgraduate medical journal. 2003; 79(929):
133-138.

6. El-Hajj Fuleihan G, Adib G, Nauroy L. The middle east & Africa regional audit, epidemiology, costs & burden of osteoporosis in 2011. International Osteoporosis Foundation. 2011: 102011-105000.

7. Moayyeri A, Soltani A, Larijani B, Naghavi M, Alaeddini F, Abolhassani F. Epidemiology of hip fracture in Iran: results from the Iranian Multicenter Study on Accidental Injuries. Osteoporosis international. 2006; 17(8): 1252-1257.

8. Alavi NM. Self treatment experience in diabetes mellitus type 2. Iranian Journal of Endocrinology and Metabolism. 2008; 10(6): 581-588.

9. Kuttler KAG, Rice K. Physician Critical Care Progress Note: A Study of the Documentation of Problems and Plans: BiblioBazaar; 2011.

10. Wong W, Glance D. Statistical semantic and clinician confidence analysis for correcting abbreviations and spelling errors in clinical progress notes. Artificial intelligence in medicine. 2011; 53(3): 171-180.

11. Wiger DE. The Clinical Documentation Sourcebook: The Complete Paperwork Resource for Your Mental Health Practice: Wiley; 2010.

12. Tang PC, LaRosa MP, Gorden SM. Use of computer-based records, completeness of documentation, and appropriateness of documented clinical decisions. Journal of the American Medical Informatics Association. 1999; 6(3): 245-251.

13. Ma HNN, Evens M, Trace DA, Naeymi-Rad F. A productive user environment for generating progress notes. Paper presented at: Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on; 14-17 Jun 1992, 1992.

14. Chan W, Centiu C, Morris JA. Uniform data standards for capturing patient medical record information at the point of care. Journal of healthcare information management: JHIM. 1999; 13(3): 85.

15. Bansler JP, Havn EC, Schmidt K, Mønsted T, Petersen HH, Svendsen JH. Cooperative epistemic work in medical practice: an analysis of physicians’ clinical notes. Computer Supported Cooperative Work (CSCW). 2016; 25(6): 503-546.

16. Sadoughi F, Nasiri S, Langarizadeh m. Perinatal Minimum Dataset for Iran: A Delphi Study. Health Information Management. 2014; 11(6): 667-680.

17. Mowery D, Wiebe J, Visweswaran S, Harkema H, Chapman WW. Building an automated SOAP classifier for emergency department reports. J Biomed Inform. 2012; 45(1): 71-81.

18. Lacasse Y, Godbout C, Series F. Health-related quality of life in obstructive sleep apnoea. European Respiratory Journal. 2002; 19(3): 499-503.

19. Cameron S, Turtle-Song I. Learning to Write Case Notes Using the SOAP Format. Journal of Counseling & Development. 2002; 80(3): 286-292.

20. Compston J, Cooper A, Cooper C, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017; 12(1): 43-43.

21. Cosman F, de Beur SJ, LeBoff MS, et al. Clinician's Guide to Prevention and Treatment of Osteoporosis. Osteoporos Int. 2014; 25(10):
2359-2381.

22. Dennison EM, Compston JE, Flahive J, et al. Effect of co-morbidities on fracture risk: findings from the Global Longitudinal Study of Osteoporosis in Women (GLOW). Bone. 2012; 50(6): 1288-1293.

23. Hooven FH, Adachi JD, Adami S, et al. The Global Longitudinal Study of Osteoporosis in Women (GLOW): rationale and study design. Osteoporos Int. 2009; 20(7): 1107-1116.

24. Iliou T, Anagnostopoulos C-N, Stephanakis IM, Anastassopoulos G. A novel data preprocessing method for boosting neural network performance: A case study in osteoporosis prediction. Information Sciences. 2017/02/20/2017; 380: 92-100.

25. Kanis JA. Assessment of osteoporosis at the primary health-care level: Technical report; 2007.

26. Mantzaris D, Anastassopoulos G, Iliadis L, Kazakos K, Papadopoulos H. A soft computing approach for osteoporosis risk factor estimation. Paper presented at: IFIP International Conference on Artificial Intelligence Applications and Innovations 2010.

27. Campbell K, Wieckert K, Fagan L, Musen M. A computer-based tool for generation of progress notes. Paper presented at: Proceedings of the Annual Symposium on Computer Application in Medical Care 1993.

28. Di Poala J. SOAP is good for medical record. Archives of internal medicine. 1992; 152(12):
2511-2511.

29. Singer EP. Why SOAP is good for the medical record?: another view. Archives of internal medicine. 1992; 152(12): 2511-2511.

30. Sorgente T, Fernandez EB, Larrondo Petrie M. The SOAP pattern For Medical Charts. Paper presented at: Proceedings of PLoP 2005.

31. Dell R, Greene D. Is osteoporosis disease management cost effective? Current osteoporosis reports. 2010; 8(1): 49-55.

32. Aggarwal N, Raveendran A, Khandelwal N, et al. Prevalence and related risk factors of osteoporosis in peri- and postmenopausal Indian women. J Midlife Health. 2011; 2(2): 81-85.

33. Osteoporosis Clinical Guidline. Second ed. Osteoporosis Research Center of Glands and Metabolism of Tehran Medical Sciences University, Tehran: Pooneh Nashr; 2005.

34. Langarizadeh m, Oji, Orooji. A Desicion Support System for prediction of Osteoporosis Health Information Management. 2019; 21(74): 87-100.

35. Rashidi H, Hafezi A, Shahbazian H, Noohjah S. Identifying Minimum Dataset for registering in record of patients with Calcuim Disorders and Osteoporosis. Jondi shapour Medical Journal. 2018; 17(3): 285-294.