Determining chronic pain data elements as a first step towards improving quality of care and research in chronic pain: Chronic pain data elements for improving quality of care

Determining chronic pain data elements as a first step towards improving quality of care and research in chronic pain

Chronic pain data elements for improving quality of care


  • Arezo Baradaran School of Allied Medical Sciences, Tehran University of Medical Sciences
  • Poupak Rahimzadeh Professor of Anesthesiology, Pain Research Center, Iran University of Medical Sciences
  • Marsa Gholamzadeh Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences
  • Leila Shahmoradi Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences


Chronic pain, Registry, Minimum dataset, Pain management


Background: Chronic pain is a significant clinical problem in the world.  There is still no quite effective treatment for this pain due to its complex nature. Timely retrieval of accurate and comprehensive information through organized clinical and epidemiological studies is an essential prerequisite for providing high quality clinical care and more accurate health planning.  We aimed to determine minimum set of data needed as a first step in design and development of a chronic pain registry system.

Materials and Methods: This descriptive-applied study was carried out in three phases; identifying necessary minimum data, preparing a primary minimum dataset, and surveying experts by questionnaire. 

Result: The literature review revealed that, the primary minimum dataset consisted of 51 elements, which were reduced to 41 after applying the experts’ opinion. This dataset covered six areas: demographic information (8 elements), initial pain assessment (12 elements), medical history (8 elements), mental health and well-being (6 elements), diagnostic measures (3 elements), and diagnosis and treatment plan (4 elements).

Conclusion: Determining minimum set of chronic pain data will be an effective step towards integrating and improving information management of patients with chronic pain. It will also allow for proper storage and retrieval of information related to these patients.


1. Bergh I, Gunnarsson M, Allwood J, Oden A, Sjöström B, Steen B. Descriptions of pain in elderly patients following orthopaedic surgery. Scand J Caring Sci 2005;19(2):110-118.
2. Potter PA, Perry AG, Stockert P, Hall A. Fundamentals of Nursing-E-Book: Elsevier Health Sciences; 2016.
3. Turk DC, Okifuji A. Psychological factors in chronic pain: Evolution and revolution. J Consult Clin Psychol 2002;70(3):678.
4. Turk D, Okifuji A, Sinclair JD, Starz T. Pain, disability, and physical functioning in subgroups of patients with fibromyalgia. J Rheumatol 1996;23(7):1255-1262.
5. Health UDo, Services H. Pain management best practices Inter-Agency Task force report: updates, gaps, inconsistencies, and recommendations. Retrieved from US Department of Health and Human Services website: https://www hhs gov/ash/advisory-committees/pain/reports/index html Accessed Dec. 2019.
6. Turk DC, Monarch ES. Biopsychosocial perspective on chronic pain. 2018.
7. Mills S, Torrance N, Smith BH. Identification and management of chronic pain in primary care: a review. Curr Psychiat Rep 2016;18(2):22.
8. Dressler AM, Gillman AG, Wasan AD. A narrative review of data collection and analysis guidelines for comparative effectiveness research in chronic pain using patient-reported outcomes and electronic health records. J Pain Res 2019;12:491.
9. Torabi M, Safdari R, Shahmoradi L. health information technology management: Jafari publication; 2010.
10. Shahmoradi L, Ahmadi M, Sadoughi F, Piri Z, Gohari MR. A comprehensive model for executing knowledge management audits in organizations: a systematic review. Health Care Manager 2015;34(1):28-40.
11. Shahmoradi L, Farzanehnejad AR. Guideline-based clinical decision support systems as an inseparable tool for better cancer care management. Iranian J Public Health 2016;45(7):962.
12. Maghooli K, Langarizadeh M, Shahmoradi L, Habibi-koolaee M, Jebraeily M, Bouraghi H. Differential diagnosis of Erythmato-Squamous Diseases using classification and regression tree. Acta Inform Medica 2016;24(5):338.
13. Safdari R, Masouri N, Ghazi Saeedi M, Sharifian R, Soltani A, Shahmoradi L. Wireless and mobile systems in telemedicine. ISMJ. 2012;15(4):327-338.
14. Torabi M, Safdari R, Shahmoradi L. health information technology management: Jafari publication; 2010.
15. Mahmoudvand Z, Kamkar M, Shahmoradi L, Nejad AF. Determination of minimum data set (msd) in echocardiography reporting system to exchange with iran’s electronic health record (ehr) system. Acta Inform Med 2016;24(2):116.
16. Safdari R, Shahmoradi L, Ebrahimi M. Minimum data set of anatomical pathology information system from the perspective of experts. J Payavard Salamat 2015;9(3):300-314.
17. Shahmoradi L, Ahmadi M, Rezaei P. Electronic health records: the structure, content and assessment. Tehran: Jafari Publications; 2008.
18. Shahraki A, Ghabaee M, Shahmoradi L, Malak J, Jazani M, Safdari R. Smart Acute Stroke Quality Registry Design-Data Elements Identification. J Registry Manag 2018;45(1):43-7.
19. Organization WH. Scoping document for WHO Treatment Guidelines on chronic non-malignant pain in adults. Geneva: WHO; 2008.
20. Management of chronic pain A national clinical guideline. Scottish Intercollegiate Guidelines Network 2013; Available from:
21. Bruce RD, Merlin J, Lum PJ, Ahmed E, Alexander C, Corbett AH, et al. 2017 HIVMA of IDSA clinical practice guideline for the management of chronic pain in patients living with HIV. Clin Infect Dis 2017;65(10):e1-e37.
22. Chou R, Gordon DB, de Leon-Casasola O, Rosenberg J, Bickler S, Brennan T. Guidelines on the management of postoperative pain. J Pain 2016;17(2):131-157.
23. Giummarra M, Arnold C, Gibson S, Dircks L, Hogg M. Victorian Persistent Pain Outcome Project: Evaluation of a minimum data set of outcome measures for chronic pain clinics. 2014.
24. Management ASoATFoCP. Practice guidelines for chronic pain management: an updated report by the American Society of Anesthesiologists Task Force on Chronic Pain Management and the American Society of Regional Anesthesia and Pain Medicine. Anesthesiology. 2010;112(4):810.
25. Rosenquist E, Arson M, Park L. Evaluation of chronic pain in adults. UpTo Date. 2018:1-14.
26. Nyberg V, Sanne H, Sjölund BH. Swedish quality registry for pain rehabilitation: purpose, design, implementation and characteristics of referred patients. J J Rehabil Med 2011;43(1):50-57.
27. Choinière M, Ware M, Pagé M, Lacasse A, Lanctôt H, Beaudet N, et al. Development and implementation of a registry of patients attending multidisciplinary pain treatment clinics: The Quebec Pain Registry. Pain Res Manag. 2017;2017.
28. Zaslansky R, Rothaug J, Chapman RC, Backström R, Brill S, Engel C, et al. PAIN OUT: an international acute pain registry supporting clinicians in decision making and in quality improvement activities. J Eval Clin Pract 2014;20(6):1090-8.
29. Vægter HB, Høybye MT, Larsen S, Hansen OB, Pedersen CB, Jensen P, et al. PainData: A clinical pain registry in Denmark. Scandi J Pain 2017;16(1):185.
30. Health Care Guideline: Assessment and Management of Chronic Pain. Institute for Clinical Systems Improvement 2009;4.
31. NICE. Chronic pain: Assessment and Management. National institute for Health and Care Excellence. 2018;final scope.
32. Paice JA, Portenoy R, Lacchetti C, Campbell T, Cheville A, Citron M, et al. Management of chronic pain in survivors of adult cancers: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol 2016;34(27):3325-3345.
33. Gordon DB, Dahl JL, Miaskowski C, McCarberg B, Todd KH, Paice JA, et al. American pain society recommendations for improving the quality of acute and cancer pain management: American Pain Society Quality of Care Task Force. Arch Int Med 2005;165(14):1574-1580.
34. Mackey S, Kao M-C, Cook K, Olson G, PAcht T, Darnall B, et al. Collaborative health outcomes information registry (CHOIR): open source cloud-based platform to generate and support learning healthcare systems. Neuromodulation 2015;18(6).
35. Lacasse A, Roy J-S, Parent AJ, Noushi N, Odenigbo C, Pagé G, et al. The Canadian minimum dataset for chronic low back pain research: a cross-cultural adaptation of the National Institutes of Health Task Force Research Standards. CMAJ open 2017;5(1):E237.
36. Griffiths D, Mitchell Noon J, Campbell F, Price C. Clinical governance and chronic pain: towards a practical solution. Anaesthesia 2003;58(3):243-248.
37. Granan L-P, Reme SE, Jacobsen HB, Stubhaug A, Ljoså TM. The Oslo University Hospital Pain Registry: development of a digital chronic pain registry and baseline data from 1,712 patients. Scand J Pain 2019;19(2):365-373.
38. Caraceni A, Cherny N, Fainsinger R, Kaasa S, Poulain P, Radbruch L, et al. Pain measurement tools and methods in clinical research in palliative care: recommendations of an Expert Working Group of the European Association of Palliative Care. J Pain Symptom Manag 2002;23(3):239-255.
39. Jamison RN, Edwards RR. Integrating pain management in clinical practice. J Clin Psychol Med Set 2012;19(1):49-64.
40. Love BL, Jensen LA, Schopflocher D, Tsui BC. Development of an electronic database for Acute Pain Service outcomes. Pain Res Manag 2012;17(1):25-30.
41. Breivik H, Borchgrevink P, Allen S, Rosseland L, Romundstad L, Breivik Hals E, et al. Assessment of pain. Br J Anaesth.2008;101(1):17-24.
42. Hansen M, Andersen TE, Armour C, Elklit A, Palic S, Mackrill T. PTSD-8: a short PTSD inventory. Clinical practice and epidemiology in mental health: CP & EMH 2010;6:101.
43. Milton MB, Börsbo B, Rovner G, Lundgren-Nilsson Å, Stibrant-Sunnerhagen K, Gerdle B. Is pain intensity really that important to assess in chronic pain patients? A study based on the Swedish Quality Registry for Pain Rehabilitation (SQRP). PLoS One 2013;8(6):e65483.
44. Hawker Ga, Samra M, Tetyana K, Melissa F. Measures of adult pain. Measures of Juvenile Fibromyalgia 133 Measures of Disability 140 Measures of Adult Pain 157 Measures of Health-Related Quality of Life in Pediatric Systemic Lupus Erythematosus 170 Measures of Fatigue 178. 2011;63(S11):S240-S252.
45. Arbabi F. Chronic Pain Impact Factors: A Review of the Different Aspects of Chronic Non Cancer Pain. J Psychol Psychother 2019;9(1):352.
46. Lawshe CH. A quantitative approach to content validity 1. Personnel psychol 1975;28(4):563-75.
47. Rutherford-Hemming T. Determining content validity and reporting a content validity index for simulation scenarios. Nurs Educ Perspect 2015;36(6):389-93.
48. Vakili MM, Jahangiri N. Content validity and reliability of the measurement tools in educational, behavioral, and health sciences research. Int J Med Educ Dev 2018;10(28):106-118.
49. Zamanzadeh V, Ghahramanian A, Rassouli M, Abbaszadeh A, Alavi-Majd H, Nikanfar A-R. Design and implementation content validity study: development of an instrument for measuring patient-centered communication. J Car Sci 2015;4(2):165.
50. Szklo M, Nieto FJ. Epidemiology: beyond the basics: Jones & Bartlett Publishers; 2014.







How to Cite

Baradaran A, Rahimzadeh P, Gholamzadeh M, Shahmoradi L. Determining chronic pain data elements as a first step towards improving quality of care and research in chronic pain: Chronic pain data elements for improving quality of care. Acta Biomed [Internet]. 2021 Sep. 2 [cited 2024 Jul. 24];92(4):e2021272. Available from: