Skip to main content
SpringerLink
Log in
Book cover

Comorbidity in Rheumatic Diseases pp 405–424Cite as

e-Comorbidity and Information Technology

  • Chapter
  • First Online:

  • 1112 Accesses

Abstract

Over the past years, technology has been the driving force behind improvements in health-care and patient management. This was supported by its positive impact on many aspects of standard medical practice, data collection, research, and treatments which allowed medical providers to use new tools in their day-to-day practice and find fresh and innovative ways to practice medicine into the future. The use of gadgets such as smartphones, tablets, applications, smart watches, as well as Web-enabled devices has also transformed our daily lives and the way people communicate and monitor their medical status. This chapter will address the latest developments regarding the use of modern technology in the standard rheumatology practice, its impact on the delivery of direct health care, services tailored to the patient, as well as real-time monitoring of the disease vital signs and comorbidity. It will not focus on what technology will be like in the future, but rather, what will rheumatologists be like.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Institute of Medicine. The computer-based patient record: an essential technology for health care. Washington, DC: National Academy Press; 1997.

    Google Scholar 

  2. Weiner M, Biondich P. The influence of information technology on patient-physician relationships. J Gen Intern Med. 2006;21(Suppl 1):S35–9.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Institute of Medicine. Crossing the quality chasm: a new health care system for the 21st century. Washington, DC: National Academy Press; 2001.

    Google Scholar 

  4. El Miedany Y, El Gaafary M, Youssef S, Palmer D. Development of the patient motivation questionnaire: conceptualizing and measuring motivation in patients with inflammatory arthritis. Ann Rheum Dis. 2016;75(Suppl2):469.

    Google Scholar 

  5. El Miedany Y, Shams K. The electronic recording of outcome measures in inflammatory arthritis: is there a room for the second generation. Ann Rheum Dis. 2009;68(Suppl3):737.

    Google Scholar 

  6. El Miedany Y. e-rheumatology: are we ready. Clin Rheumatol. 2015;34(5):831–7.

    Article  PubMed  Google Scholar 

  7. El Miedany Y, El Gaafary M, Youssef S, Palmer D. Electronic patient reported outcome measures (e-PROMS) for early arthritis in standard clinical practice: a pilot study. Ann Rheum Dis. 2015;74(Suppl 2):65.

    Google Scholar 

  8. Text of the Telehealth Modernization Act of 2013. Accessed 25 June 2014. https://www.govtrack.us/congress/bills/113/hr3750/text.

  9. American Telemedicine Association. What is telemedicine? Accessed 24 June 2014. https://s3.amazonaws.com/rdcms-himss/files/production/public/HIMSSorg/Content/files/Line%2016%20- %20What%20Is%20Telemedicine.pdf.

  10. Doarn CR, et al. Federal efforts to define and advance telehealth – a work in progress. Telemed J E Health. 2014;20(5):409–18. Accessed 25 June 2014

    Article  PubMed  PubMed Central  Google Scholar 

  11. National Collaborating Centre for Chronic Conditions. Rheumatoid arthritis: national clinical guideline for management and treatment in adults. London: Royal College of Physicians; 2009.

    Google Scholar 

  12. Palmer D, El Miedany Y. PROMs: a novel approach to arthritis self-management. Br J Nurs. 2012;21(10):601–5.

    Article  PubMed  Google Scholar 

  13. Palmer D, El Miedany Y. EROMIA in inflammatory arthritis: the next step in standard practice. Br J Nurs. 2010;19(1):42–6.

    Article  PubMed  Google Scholar 

  14. Varroud-Vial M. Improving diabetes management with electronic medical records. Diabetes Metab. 2011;37(Suppl 4):S48–52.

    Article  PubMed  Google Scholar 

  15. Meng X, Yang J-J. Visual analysis for type 2 diabetes mellitus – based on electronic medical records, Chapter Smart Health Volume 8549 of the series Lecture notes in computer science. Cham: Springer; 2014. p. 160–70.

    Google Scholar 

  16. Jaspen B. CVA and IBM’s Watson cloud pursue ways to predict patient health. Forbes.com. 2015. http://www.forbes.com/sites/brucejapsen/2015/07/30/cvs-and-ibms-watson-partner-to-predict-patient-health-needs/#431d14c68ac9.

  17. Furukawa M, King J, Patel V, Hsiao CJ, Adler-Milstein J, Jha A. Despite substantial progress in EHR adoption, health information exchange and patient engagement remain low in office settings. Health Aff. 2014;33:1672–9. doi:10.1377/hlthaff.2014.0445. http://content.healthaffairs.org/content/early/2014/08/05/hlthaff.2014.0445

    Article  Google Scholar 

  18. Mercy Health Coach Program. https://www.mercydesmoines.org/healthcoach.

  19. Demiris G, Afrin LB, Speedie S, et al. Patient-centered applications: use of information technology to promote disease management and wellness. A white paper by the AMIA knowledge in motion working group. J Am Med Inform Assoc. 2008;15:8–13.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Ross SE, Todd J, Moore LA, et al. Expectations of patients and physicians regarding patient-accessible medical records. J Med Internet Res. 2005;7:e13.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ferreira A, Correia A, Silva A, et al. Why facilitate patient access to medical records. Med Care Compunetics. 2007;4:77–90.

    Google Scholar 

  22. Honeyman A, Cox B, Fisher B. Potential impacts of patient access to their electronic health records. Inform Prim Care. 2005;13:55–60.

    PubMed  Google Scholar 

  23. Van der Vaart R, Drossaert CHC, Taal E, et al. Patient preferences for a hospital based rheumatology Interactive Health Communication Application, and factors associated with these preferences. Rheumatology. 2011;50:1618–26.

    Article  PubMed  Google Scholar 

  24. Van der Vaart R, Drossaert CHC, Taal E, et al. Experiences and preferences of patients with rheumatic diseases regarding an interactive health communication application. In: Proceedings of the 2nd eTELEMED international conference on ehealth, telemedicine, and social medicine, Saint Martin; 2010 Feb, p. 64–71. doi:10.1109/eTELEMED.2010.16.

  25. Richter JG, Becker A, Koch T, et al. Changing attitudes towards online electronic health records and online patient documentation in rheumatology outpatients. Clin Exp Rheumatol. 2010;28:261–4.

    PubMed  Google Scholar 

  26. Masys D, Baker D, Butros A, et al. Giving patients access to their medical records via the internet: the PCASSO experience. J Am Med Inform Assoc. 2002;9:181–91.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Reti SR, Feldman HJ, Ross SE, et al. Improving personal health records for patient-centered care. J Am Med Inform Assoc. 2010;17:192–5.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Earnest M, Ross SE, Wittevrongel L, et al. Use of a patient-accessible electronic medical record in a practice for congestive heart failure: patient and physician experiences. J Am Med Inform Assoc. 2004;11:410.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Winkelman WJ, Leonard KJ. Overcoming structural constraints to patient utilization of electronic medical records: a critical review and proposal for an evaluation framework. J Am Med Inform Assoc. 2004;11:151–5.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Leonard K, Wiljer D, Casselman M. An innovative information paradigm for consumers with chronic conditions: the value proposition. J Inf Tech Health Care. 2008;6:274–82.

    Google Scholar 

  31. El Miedany Y. Joint fitness programme for arthritic patients – the value of patient-reported outcome measures in designing a tailored approach to patient education. Eur Musculoskelet Rev. 2012.; https://www.touchmusculoskeletal.com

  32. Fridsma D. Interoperability vs health information exchange: setting the record straight. Accessed 11 June 2014. https://www.healthit.gov/buzz-blog/meaningful-use/interoperability-health-information-exchange-settingrecord- straight/.

  33. Healthcare Information and Management Systems Society. What is interoperability? Accessed 8 June 2014. http://www.himss.org/library/interoperability-standards/what-is-interoperability.

  34. El Miedany Y, El Gaafary M, Sayed S, Palmer D, Ahmed A. Implementing shared decision making in clinical practice: outcomes of new shared decision making aid for chronic inflammatory arthritis patients. J Pat Care. 2016;2(2):117–23.

    Google Scholar 

  35. Palmer D, El Miedany Y. Shared decision making for patients living with inflammatory arthritis. Br J Nurs. 2016;25(1):31–5.

    Article  PubMed  Google Scholar 

  36. Pew Research Internet Project. http://www.pewinternet.org/2013/01/15/health-online-2013/.

  37. Berland GK, Elliott MN, Morales LS, et al. Health information on the Internet: accessibility, quality, and readability in English and Spanish. JAMA. 2001;285(20):2612–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tang H, Kwoon J. Googling for a diagnosis-use of Google as a diagnostic aid: Internet based study. BMJ. 2006; doi:10.1136/bmj.39003.640567.AE.

  39. Albert D, Merrihew K, Pletcher S. Tele-rheumatology: the future is now [abstract]. Arthritis Rheum. 2012;64(Suppl 10):2053. doi:10.1002/art.39785.

    Google Scholar 

  40. Health Tap Inc. [US]. https://www.healthtap.com/.

  41. Ringadoc. https://www.ringadoc.com/.

  42. Berenbaum F. The social (media) side to rheumatology. Nat Rev Rheumatol. 2014;10:314–31.

    Article  PubMed  Google Scholar 

  43. John Hopkins Kimmel Cancer in Baltimore. Doctors often use social media on the job: survey. 2013. http://health.usnews.com/health-news/news/articles/2013/01/01/docs-often-use-social-media-on-the-job-survey.

  44. Google+. Rheumatology world [online]. 2015. https://plus.google.com/communities/108852894746535117171.

  45. Mayo clinic App. 2015. http://www.mayoclinic.org/apps/mayo-clinic.

  46. El Miedany Y, El Gaafary M, Youssef S, Bahlas S, Almedany S, Ahmed I, Palmer D. Toward electronic health recording: evaluation of electronic patient-reported outcome measures system for remote monitoring of early rheumatoid arthritis. J Rheumatol. 2016;43(12):2106–12.

    Article  PubMed  Google Scholar 

  47. El Miedany Y, El Gaafary M, El Aroussy M, Bahlas S, Hegazi M, Palmer D, Youssef S. Toward electronic health recording: evaluation of electronic patient reported outcome measures (e-PROMs) system for remote monitoring of early systemic lupus patients. Clin Rheumatol. 2017; doi:10.1007/s10067-017-3675-9.

  48. Fox S, Fallows D. Health searches and email have become more commonplace, but there is room for improvement in searches and overall Internet access. 2003. http://www.pewinternet.org/files/old-media//Files/Reports/2003/PIP_Health_Report_July_2003.pdf.pdf.

    Google Scholar 

  49. El Miedany Y, El Gaafary M, Palmer D. Assessment of the utility of visual feedback in the treatment of early rheumatoid arthritis patients: a pilot study. Rheumatol Int. 2012;32:3061–8.

    Article  PubMed  Google Scholar 

  50. Bosworth HB, Granger BB, Mendys P. Medication adherence: a call for action. Am Heart J. 2011;162(3):412–24.

    Article  PubMed  PubMed Central  Google Scholar 

  51. El Miedany Y, El Gaafary M, Youssef S, Bahlas S, Ahmed I, Hegazi M. Rheumatoid arthritis comorbidity index: development and validation of a new specific tool for classifying prognostic comorbidity in rheumatoid arthritis patients. Ann Rheum Dis. 2016;75(Suppl2):154.

    Google Scholar 

  52. El Miedany Y, El Gaafary M, Youssef S, Bahlas S, Ahmed I, Hegazi M. Psoriatic arthritis Comorbidity Index: development and validation of a new specific tool for classifying prognostic comorbidity in psoriatic arthritis patients. Ann Rheum Dis. 2016;75(Suppl2):89.

    Google Scholar 

  53. El Miedany YM, El Gaafary M, Youssef S, Bahlas S, Hegazi M, Ahmed I. E-comorbidity: evaluation of the validity of electronic comorbidity assessment in identifying comorbid conditions among patients with rheumatoid and psoriatic arthritis. Arthritis Rheumatol. 2016; 68 (suppl 10).

    Google Scholar 

  54. Hunter DJ. Lower extremity osteoarthritis management needs a paradigm shift. Br J Sports Med. 2011;45:283–8.

    Article  PubMed  Google Scholar 

  55. National Institute for Health and Clinical Excellence. Osteoarthritis: care and management in adults, Clinical guideline CG177. London: NICE; 2014. https://www.nice.org.uk/guidance/cg177

    Google Scholar 

  56. Bennell KL, Kyriakides M, Hodges PW, et al. Effects of two physiotherapy booster sessions on outcomes with home exercise in people with knee osteoarthritis: a randomized controlled trial. Arthritis Care Res (Hoboken). 2014;66:1680–7.

    Article  Google Scholar 

  57. Swan M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health. 2009;6:492–525.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Bonato P. Wearable sensors and systems. From enabling technology to clinical applications. IEEE Eng Med Biol Mag. 2010;29:25–36.

    Article  PubMed  Google Scholar 

  59. Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors (Basel). 2014;14:3362–94.

    Article  Google Scholar 

  60. Lukowicz P, Kirstein T, Troster G. Wearable systems for health care applications. Methods Inf Med. 2004;43:232–8.

    CAS  PubMed  Google Scholar 

  61. Shull PB, Jirattigalachote W, Hunt MA, et al. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture. 2014;40:11–9.

    Article  PubMed  Google Scholar 

  62. Bergmann JHM, McGregor AH. Body-worn sensor design: what do patients and clinicians want? Ann Biomed Eng. 2011;39:2299–312.

    Article  CAS  PubMed  Google Scholar 

  63. Appelboom G, Camacho E, Abraham ME, et al. Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health. 2014;72:28.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Singh A, Klapper A, Jia J, et al. Motivating people with chronic pain to do physical activity: opportunities for technology design. In: ACM proceedings of the 33rd ACM conference on human factors in computing systems, New York; 2014, p. 2803–12.

    Google Scholar 

  65. Renaud K, van Biljon J. Predicting technology acceptance and adoption by the elderly: a qualitative study. In: Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology, Wilderness; 2008, p. 210–19.

    Google Scholar 

  66. Papi E, Belsi A, McGregor AH. A knee monitoring device and the preferences of patients living with osteoarthritis: a qualitative study. BMJ Open. 2015;5:e007980.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Vize R. Google search vs doctor diagnosis – always an imperfect balance. https://www.theguardian.com/healthcare-network/2015/jun/19/google-search-doctor-diagnosis-imperfect-balance-bronte-doyne-cancer.

  68. White R, Horvitz E. Cyberchondria: studies of the escalation of medical concerns in web search. Microsoft research. 2008. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/TR-2008-178.pdf.

  69. Elkin PL, Liebow M, Bauer BA, Chaliki S, Wahner-Roedler D, Bundrick J, Lee M, Brown SH, Froehling D, Bailey K, Famiglietti K, Kim R, Hoffer E, Feldman M, Barnett GO. The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs). Int J Med Inform. 2010;79(11):772–7.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. King’s College, London, UK

    Yasser El Miedany MD, FRCP

  2. Rheumatology and Rehabilitation, Ain Shams University, Cairo, Egypt

    Yasser El Miedany MD, FRCP

  3. Department of Rheumatology, Darent Valley Hospital, Dartford, Kent, UK

    Yasser El Miedany MD, FRCP

Authors
  1. Yasser El Miedany MD, FRCP
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasser El Miedany MD, FRCP .

Editor information

Editors and Affiliations

  1. Consultant Rheumatologist, Department of Rheumatology, Darent Valley Hospital, Dartford, Kent, United Kingdom

    Yasser El Miedany

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

El Miedany, Y. (2017). e-Comorbidity and Information Technology. In: El Miedany, Y. (eds) Comorbidity in Rheumatic Diseases. Springer, Cham. https://doi.org/10.1007/978-3-319-59963-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59963-2_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59962-5

  • Online ISBN: 978-3-319-59963-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation