Document Type : Original Article


1 Department of Health Information Technology, Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

3 Department of Oral Medicine, Dental Research Center, Faculty of Dentistry, Tehran University of Medical Sciences, Tehran, Iran

4 Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran

5 Department of Oral and Maxillofacial Medicine, Tehran University of Medical Sciences, Tehran, Iran


BACKGROUND: Oral soft tissue diseases include a broad spectrum, and the wide array of patient
data elements need to be processed in their diagnosis. One of the biggest and most basic challenges
is the analysis of this huge amount of complex patient data in an increasing number of complicated
clinical decisions. This study seeks to identify the necessary steps for collecting and management
of these data elements through establishing a consensus‑based framework.
METHODS: This research was conducted as a descriptive, cross‑sectional study from April 2016 to
January 2017, which has been performed in several steps: literature review, developing the initial
draft (v. 0), submitting the draft to experts, validating by an expert panel, applying expert opinions
and creating version v.i, performing Delphi rounds, and creating the final framework.
RESULTS: The administrative data category with 17 and the historical data category with 23
data elements were utilized in recording data elements in the diagnosis of all of the different oral
diseases. In the paraclinical indicator and clinical indicator categories, the necessary data elements
were considered with respect to the 6 main axes of oral soft tissue diseases, according to Burket’s
Oral Medicine: ulcerative, vesicular, and bullous lesions; red and white lesions of the oral mucosa;
pigmented lesions of the oral mucosa; benign lesions of the oral cavity and the jaws; oral and
oropharyngeal cancer; and salivary gland diseases.
CONCLUSIONS: The study achieved a consensus‑based framework for the essential data element
in the differential diagnosis of oral medicine using a comprehensive search with rich keywords in
databases and reference texts, providing an environment for discussion and exchange of ideas
among experts and the careful use of the Delphi decision technique.


1. Glick M. Burket’s Oral Medicine, 12e. Shelton: PMPH‑USA; 2015.
2. Neville BW, Damm DD, Chi AC, Allen CM. Oral and Maxillofacial
Pathology. Elsevier Health Sciences; United States; 2015.
3. Nikitakis NG. Oral soft tissue lesions: A guide to differential
diagnosis Part II: Surface alterations. Br J Oral Sci 2015;4:707‑15.
4. Hussain F, Cooper A, Carson‑Stevens A, Donaldson L, Hibbert P,
Hughes T, et al. Diagnostic error in the emergency department:
Learning from national patient safety incident report analysis.
BMC Emerge Med 2019;19:77.
5. Newman‑Toker DE, Pronovost PJ. Diagnostic errors‑the next
frontier for patient safety. Jama 2009;301:1060‑2.
6. El‑Kareh R, Hasan O, Schiff GD. Use of health information
technology to reduce diagnostic errors. BMJ Quality Safety
2013;22 Suppl 2:ii40‑51.
7. Stone E, Rankin N, Phillips J, Fong K, Currow DC, Miller A, et al.
Consensus minimum data set for lung cancer multidisciplinary
teams: Results of a Delphi Process. Respirology 2018;23:927‑34.
8. Cadilhac DA, Bagot KL, Demaerschalk BM. Establishment
of an internationally agreed minimum data set for acute
telestroke. J Telemed Telecare. January 14, 2020. doi.
9. Amos KJ, Bearman M, Palermo C. Evidence regarding teaching
and assessment of record‑keeping skills in training of dental
students. J Dent Educ 2015;79:1222‑9.
10. Rhodes P. Electronic clinical records: Having the right data to
navigate through the perfect storm. J California Dent Assoc
11. Ginnis J, Ferreira Zandoná AG, Slade GD, Cantrell J, Antonio ME,
Pahel BT, et al. Measurement of early childhood oral health for
research purposes: Dental caries experience and developmental
defects of the enamel in the primary dentition. Methods Mol Biol
12. Millonig MK. Mapping the route to medication therapy
management documentation and billing standardization
and interoperabilility within the health care system: Meeting
proceedings. J Am Pharm Assoc 2009;49:372‑82.
13. Felix DH, Luker J, Scully C. Oral medicine: 3. Ulcers: Cancer.
Dental update 2012;39:664‑8, 670.
14. Sarrion Perez MG, Bagan JV, Jimenez Y, Margaix M, Marzal C.
Utility of imaging techniques in the diagnosis of oral cancer.
J Cranio‑Maxillo‑Facial Surg 2015;43:1880‑94.
15. Varela‑Centelles P, Lopez‑Cedrun JL, Fernandez‑Sanroman J,
Seoane‑Romero JM, Santos de Melo N, Alvarez‑Novoa P, et al.
Key points and time intervals for early diagnosis in symptomatic
oral cancer: A systematic review. Int J Oral Maxillofacial Surg
16. Lima AF, de Oliveira Melo T. Nurses’ perception regarding
the implementation of computer‑based clinical nursing
documentation. Rev da Escola de Enfermagem da U S P
17. Tarantino U, Giai Via A, Macri E, Eramo A, Marino V, Marsella LT.
Professional liability in orthopaedics and traumatology in Italy.
Clin Orthop Related Res 2013;471:3349‑57.
18. Kruger A. The Scandinavian pre‑hospital physician and
documentation of clinical data in the trauma patient. Scand
J Trauma Resusc Emerg Med 2013;.21(Suppl 1):S15. doi:
19. Finkelstein MW. A Guide to Clinical Differential Diagnosis of
Oral Mucosal Lesions; 2010. Available from: https://dentalcare.
com. [Last retrieved on 2010 Apr 26].
20. Masic F. Information systems in dentistry. Acta Inform Med
21. Ehtesham H, Safdari R, Mansourian A, Tahmasebian S,
Mohammadzadeh N, Pourshahidi S. Developing a new intelligent
system for the diagnosis of oral medicine with case‑based
reasoning approach. Oral Dis 2019;25:1555‑63.
22. Markowitz K, Roberts E, Strickland M. Dental products and
evidence‑based dentistry. Quintessence Int 2019;50:402‑11.
23. Kazemi‑Arpanahi H, Vasheghani‑Farahani A, Baradaran A,
Mohammadzadeh N, Ghazisaeedi M. Developing a minimum
data set (MDS) for cardiac electronic implantable devices
implantation. Acta Inform Med 2018;26:164‑8.
24. Damanabi S, Abdolnejad S, Karimi G. Suggested minimum data
set for speech therapy centers affiliated to Tabriz University of
Medical Sciences. Acta Inform Med 2015;23:243‑7.