Document Type : Original Article
Authors
1 Department of Medical Library and Information Sciences, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
2 Department of Medical Library and Information Sciences, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran, Health Management and Economics Research Center, Iran University of Medical Sciences, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
3 Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran
4 Department of Medical Library and Information Sciences, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran, Health Management and Economics Research Center, Iran University of Medical Sciences, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran,
Abstract
BACKGROUND: The prevalence of diabetes makes considerable costs for health‑care organizations.
The increase of patient’s self‑care abilities by use of personalizing health information prescription
can reduce these costs. This study was conducted to explore the benefits and challenges related to
personalizing health information prescription in diabetes clinical settings.
MATERIALS AND METHODS: The samples included diabetes education officials working in
specialized diabetes clinics and Diabetes Research Centre managers of Iran and Tehran Universities
of Medical Sciences. They were 21 cases and selected through purposeful sampling method.
Semi‑structured interview and focus discussion groups were used to collect the viewpoints of
specialists. Interview guide, based on literature review and the documents of diabetes, was used
in interviews and focus groups. Their validity was affirmed by specialists. The interview texts were
coded in MAXQDA10 software and analyzed through content analysis method.
RESULTS: The most important benefits of personalizing health information prescription were
classified into five themes as follows: medical services improvement, facilitation of consumers to
information resources, improvement in patients’ knowledge and awareness, increase in self‑care
ability and disease management, reinforcing the relation between physician and patient and keeping
physician in the information prescription cycle. The challenges of personalizing of health information
prescription were revealed as follows: Recognition of patients’ personal characteristics at the turn
of entering the system, systems’ functional modifiers especially bilateral interaction and relation to
patient’s health file, content recognition, and creating suitable protocol.
CONCLUSION: This study showed that diabetes clinical settings face different organizational and
process challenges for establishing the personalization of health information prescription. The most
important challenges which should be considered in designing information prescription in diabetes
clinical environments are as follows: reinforcing physicians’ recognition of information prescription
benefits, lack of integrative electronic health information system, and patient primary assessment
in the first stage of entering the patient into the system in respect of clinical and personal aspects
in information needs of consumer.
Keywords
- World Health Organization. Non‑Communicable Diseases. World
Health Organization; 2015. Available from: http://www.who.
int/mediacentre/factsheets/fs355/en/. [Last accessed on 2019
Dec 16].
2. Gazvin University of Medical Sciences. Self‑care management.
Special Issue Self Care 2013;2014:1‑4.
3. Zeinali V, Riahinia N, Javadi PV, Asadi S. Effect of health
information prescription(Hip) on caregiver’s self‑care ability. Hum
Informat Interact 4 (1).16‑27. Available from: http://hii.khu.ac.ir
/article‑1‑2696‑en.html. [Last accessed on 2019 Dec 19].
4. World Health Organization. Global report on Diabetes. Report No:
9789241565257. World Health Organization; 2016. [Last accessed
on 2020 Mar 02].
5. Shahrud University of Medical Sciences. The Third Meeting of
the National Iranian Cohort; 2016.
6. Gavgani VZ. Ubiquitous Information Therapy Service through
Social Networking Libraries: An Operational Web 2.0 Service
Model. User‑Driven Healthcare and Narrative Medicine: Utilizing
Collaborative Social Networks and Technologies: Utilizing
Collaborative Social Networks and Technologies; 2010. p. 446.
7. Gavgani VZ, Shokraneh F. Information therapy (Ix) and
information prescription: A systematic review. Int J User Driven
Healthc 2013;3:9‑19.
8. Oliver KB, Lehmann HP, Wolff AC, Davidson LW, Donohue PK,
Gilmore MM, et al. Evaluating information prescriptions in two
clinical environments. J Med Lib Assoc 2011;99:237‑46.
9. McKnight M. Information prescriptions, 1930‑2013: An
international history and comprehensive review. J Med Lib Assoc
2014;102:271‑80.
10. Rylance A. Using information prescriptions in diabetes. Nurs
Times 2015;111:12‑3.
11. Packard VL. Encyclopedia of Information Science and Technology.
Reference Reviews. 2018;32(5).1‑2.
12. Jayatilaka AD, Arunatileka S, Premaratne R, editors. Personalized
Web Information Retrieval Based on Varying Health Parameters
Related to Diabetes. China: Trends in Innovative Computing;
2012.
13. Koonce TY, Giuse NB, Kusnoor SV, Hurley S, Ye F. Apersonalized
approach to deliver health care information to diabetic patients
in community care clinics. J Med Libr Assoc 2015;103:123.
14. Godman B, Finlayson AE, Cheema PK, Zebedin‑Brandl E,
Gutiérrez‑Ibarluzea I, Jones J, et al. Personalizing health care:
Feasibility and future implications. BMC Med 2013;11:179.
15. Kulzer B, Daenschel W, Daenschel I, Schramm W, Messinger D,
Weissmann J, et al. Integrated personalized diabetes management
improves glycemic control in patients with insulin‑treated type 2
diabetes: Results of the PDM‑ProValue study program. Diabetes
Res Clin Pract 2018;144:200‑12.
16. Graneheim UH, Lindgren BM, Lundman B. Methodological
challenges in qualitative content analysis: A discussion paper.
Nurse Educ Today 2017;56:29‑34.
17. Weymann N, Härter M, Dirmaier J. A tailored, interactive health
communication application for patients with type 2 diabetes:
Study protocol of a randomised controlled trial. BMC Med
Informa Decision Making 2013;13:24.
18. Fried TR, Tinetti M, Agostini J, Iannone L, Towle V. Health
outcome prioritization to elicit preferences of older persons with
multiple health conditions. Patient Educ Counsell 2011;83:278‑82.
19. Grant RW, Wexler DJ. Personalized medicine in Type 2 diabetes:
What does the future hold? Diabetes Manage (London, England)
2012;2:199. - 20. Bond GE, Burr RL, Wolf FM, Feldt K. The effects of a web‑based
intervention on psychosocial well‑being among adults aged 60
and older with diabetes. Diabetes Educator 2010;36:446‑56.
21. Pal K, Eastwood SV, Michie S, Farmer AJ, Barnard ML, Peacock R,
et al. Computer-based diabetes self-management interventions for
adults with type 2 diabetes mellitus. Cochrane Database of Syst
Rev 2013;(3):1‑25. [doi.org/10.1002/14651858.CD008776.pub2].
22. Ramadas A, Quek KF, Chan C, Oldenburg B. Web‑based
interventions for the management of type 2 diabetes mellitus:
a systematic review of recent evidence. Int J Med Informat
2011;80:389‑405.
23. Cremers HP, Mercken L, Oenema A, de Vries H. A web‑based
computer‑tailored smoking prevention programme for primary
school children: Intervention design and study protocol. BMC
Public Health 2012;12:277.
24. Hamine S, Gerth‑Guyette E, Faulx D, Green BB, Ginsburg AS.
Impact of mHealth chronic disease management on treatment
adherence and patient outcomes: A systematic review. J Med
Internet Res 2015;17:e52.
25. Van Netten JJ, Woodburn J, Bus SA. The future for diabetic
foot ulcer prevention: A paradigm shift from stratified
healthcare towards personalized medicine. Diabetes
Metab Res Rev 2020;36:e3234.
26. Norouzi S, Ghalibaf AK, Sistani S, Banazadeh V, Keykhaei F,
Zareishargh P, et al. A mobile application for managing diabetic
patients’ nutrition: A food recommender system. Arch Iran Med
2018;21:466‑72.
27. Mohebbi B, Tol A, Sadeghi R, Mohtarami SF, Shamshiri A.
Self‑management intervention program based on the health belief
model (HBM) among women with gestational diabetes mellitus:
A quazi‑experimental study. Arch Iran Med 2019;22:168‑73.
28. Tol A, Baghbanian A, Mohebbi B, Shojaeizadeh D, Azam K,
Shahmirzadi SE, et al. Empowerment assessment and influential
factors among patients with type 2 diabetes. J Diabetes Metab
Disord 2013;12:6.
29. Tara M. Aspects of Information Tailoring in the 21st Century.
Encyclopedia of Information Science and Technology. 3rd ed.
United States: Pennsylvania, IGI Global; 2015. p. 4042‑52.
30. The British Diabetic Association. Information Prescriptions
for People with Diabetes 2020. Available from: https://www.
diabetes.org.uk/guide‑to‑diabetes/managing‑your‑diabetes/
information‑prescriptions. [Last accessed on 2020 May 15].
31. Liang X, Wang Q, Yang X, Cao J, Chen J, Mo X, et al. Effect of
mobile phone intervention for diabetes on glycaemic control:
A meta-analysis. Diabetic Med 2011;28:455‑63.
32. Randhawa GK, Shachak A, Courtney KL, Kushniruk A.
Evaluating a post‑implementation electronic medical record
training intervention for diabetes management in primary care.
BMJ Health Care Informat 2019;26:e100086.
)