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Leveraging the electronic health record to improve quality and safety in rheumatology

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Abstract

During the last two decades, improving the quality and safety of healthcare has become a focus in rheumatology. Widespread use of electronic health records (EHRs) and the availability of digital data have the potential to drive quality improvement, improve patient outcomes, and prevent adverse events. In the coming years, developing and leveraging tools within the EHR will be the key to making the next big strides in improving the health of patients with rheumatoid arthritis and other rheumatic diseases, including building EHR infrastructure to capture patient outcomes and developing automated methods to retrieve information from free text of clinical notes.

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References

  1. Dragoi RG, Ndosi M, Sadlonova M et al (2013) Patient education, disease activity and physical function: can we be more targeted? A cross sectional study among people with rheumatoid arthritis, psoriatic arthritis and hand osteoarthritis. Arthrits Res Ther 15(5):R156

    Article  Google Scholar 

  2. Kjeken I, Dagfinrud H, Mowinckel P et al (2006) Rheumatology care: involvement in medical decisions, received information, satisfaction with care, and unmet health care needs in patients with rheumatoid arthritis and ankylosing spondylitis. Arthritis Rheum 55(3):394–401

    Article  PubMed  Google Scholar 

  3. Nota I, Drossaert CH, Taal E et al (2014) Patient participation in decisions about disease modifying anti-rheumatic drugs: a cross-sectional survey. BMC Musculoskelet Disord 15:333

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bourgeois FT, Shannon MW, Valim C et al (2010) Adverse drug events in the outpatient setting: an 11-year national analysis. Pharmacoepidemiol Drug Saf 19(9):901–910

    Article  PubMed  PubMed Central  Google Scholar 

  5. Russo E, Sittig DF, Murphy DR et al (2016) Challenges in patient safety improvement research in the era of electronic health records. Healthc (Amst) 4(4):285–290. doi:10.1016/j.hjdsi.2016.06.005

    Article  Google Scholar 

  6. Smolen JS, Aletaha D, Bijlsma JWJ et al (2010) Treating rheumatoid arthritis to target: recommendations of an international task force. Ann Rheum Dis 69(4):631–637

    Article  PubMed  PubMed Central  Google Scholar 

  7. Institute of Medicine (2012) The learning health care system in America. National Academies Press, Washington, DC

    Google Scholar 

  8. Schoels M, Knevel R, Aletaha D et al (2010) Evidence for treating rheumatoid arthritis to target: results of a systematic literature search. Ann Rheum Dis 69:638–643

    Article  PubMed  PubMed Central  Google Scholar 

  9. Grigor C, Capell H, Stirling A et al (2004) Effect of a treatment strategy of tight control for rheumatoid arthritis (the TICORA study): a single-blind randomised controlled trial. Lancet 364:263–269

    Article  PubMed  Google Scholar 

  10. Verstappen SMM, Jacobs JWG, van der Veen MJ et al (2007) Intensive treatment with methotrexate in early rheumatoid arthritis: aiming for remission. Computer Assisted Management in Early Rheumatoid Arthritis (CAMERA, an open-label strategy trial). Ann Rheum Dis 66:1443–1449

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Allaart CF, Geokoop-Ruiterman YPM, de Vries-Bouwstra JK et al (2006) Aiming at low disease activity in rheumatoid arthritis with initial combination therapy or initial monotherapy strategies: the BeSt study. Clin Exp Rheumatol 24(6 Suppl 43):S-77–S-82

    Google Scholar 

  12. Alemao E, Joo S, Kawabata H et al (2016) Effects of achieving target measures in rheumatoid arthritis on functional status, quality of life, and resource utilization: analysis of clinical practice data. Arthritis Care Res (Hoboken) 68(3):308–317

    Article  CAS  Google Scholar 

  13. Curtis JR, Shan Y, Harrold L et al (2013) Patient perspectives on achieving treat-to-target goals: a critical examination of patient-reported outcomes. Arthritis Care Res (Hoboken) 65(10):1707–1712

    Google Scholar 

  14. Lavallee DC, Chenok KE, Love RM et al (2016) Incorporating patient-reported outcomes into health care to engage patients and enhance care. Health Aff (Millwood) 35(4):575–582

    Article  Google Scholar 

  15. National Quality Forum (2016) NQF-endorsed measures for musculoskeletal conditions. http://www.qualityforum.org/Publications/2015/01/NQF-Endorsed_Measures_for_Musculoskeletal_Conditions.aspx. Accessed 30 Jan 2017

  16. Yazdany J, Robbins M, Schmajuk G et al (2016) Development of the American College of Rheumatology’s rheumatoid arthritis electronic clinical quality measures. Arthritis Care Res 68(11):1579–1590

    Article  Google Scholar 

  17. Yazdany J, Bansback N, Clowse MEB et al (2016) Practice-level variation in quality of care in the Acr’s Rheumatology Informatics System for Effectiveness (RISE) Registry [abstract]. Arthritis Rheumatol 68(suppl 10). http://acrabstracts.org/abstract/practice-level-variation-in-quality-of-care-in-the-acrs-rheumatology-informatics-system-for-effectiveness-rise-registry/. Accessed 30 Jan 2017

  18. Desai SP, Liu CC, Tory H et al (2014) Rheumatoid arthritis quality measures and radiographic progression. Semin Arthritis Rheum 44(1):9–13

    Article  PubMed  PubMed Central  Google Scholar 

  19. Navarro-Compan V, Smolen JS et al (2015) Quality indicators in rheumatoid arthritis: results from the METEOR database. Rheumatology (Oxford, England) 54(9):1630–1639

    Article  Google Scholar 

  20. Sapir T, Rusie E, Greene L et al (2015) Influence of continuing medical education on rheumatologists’ performance on national quality measures for rheumatoid arthritis. Rheumatol Ther 2(2):141–151

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yazdany J, Bansback N, Clowse M et al (2016) Rheumatology informatics system for effectiveness: a national informatics-enabled registry for quality improvement. Arthritis Care Res 68(12):1866–1873

    Article  Google Scholar 

  22. Walsh SH (2004) The clinician’s perspective on electronic health records and how they can affect patient care. Br Med J 328:1184–1187

    Article  Google Scholar 

  23. Newman E, Sharma T, Meadows A et al (2015) Rheumatoid arthritis quality measures—automated display of care gaps and capture of physician decision making at the clinic visit [abstract]. Arthritis Rheumatol 67(suppl 10). http://acrabstracts.org/abstract/rheumatoid-arthritis-quality-measures-automated-display-of-care-gaps-and-capture-of-physician-decision-making-at-the-clinic-visit/. Accessed 25 Jan 2017

  24. Powsner SM, Wyatt JC, Wright P (1998) Opportunities for and challenges of computerisation. Lancet 352:1617–1622

    Article  CAS  PubMed  Google Scholar 

  25. Brown T (2008) Design thinking. Harv Bus Rev 862008:84–92

    Google Scholar 

  26. McCreary L (2010) Kaiser Permanente’s innovation on the front lines. Harv Bus Rev 7. https://hbr.org/2010/09/kaiser-permanentes-innovation-on-the-front-lines

  27. Carroll RJ, Thompson WK, Eyler AE et al (2012) Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc JAMIA 19(e1):e162–e169

    Article  PubMed  Google Scholar 

  28. Cannon GW, Mehortra S, South B et al (2016) A natural language processing system can capture rheumatoid arthritis disease activity measures in US veterans across multiple sites [abstract]. Arth Rheum 68(suppl 10). http://acrabstracts.org/abstract/a-natural-language-processing-system-can-capture-rheumatoid-arthritis-disease-activity-measures-in-us-veterans-across-multiple-sites/. Accessed 22 Jan 2017

  29. Liao KP, Cai T, Gainer V et al (2010) Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken) 62(8):1120–1127

    Article  Google Scholar 

  30. Lin C, Karlson EW, Dligach D et al (2015) Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record. J Am Med Inform Assoc 22(e1):e151–e161

    Article  PubMed  Google Scholar 

  31. Hernandez Boussard T, Tamang S, Blayney D et al (2016) New paradigms for patient-centered outcomes research in electronic medical records: an example of detecting urinary incontinence following prostatectomy. EGEMS (Wash DC) 4(3):1231

    Google Scholar 

  32. Tamang S, Patel MI, Blayney DW et al (2015) Detecting unplanned care from clinician notes in electronic health records. J Oncol Pract 11(3):e313–e319

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tamang S, Podchiyska T, Gyuang E et al (2015) Electronic phenotyping for measuring quality of care. American Medical Informatics Association Joint Summit: Clinical Research Informatics [abstract]. https://knowledge.amia.org/amia-59309-cri2015-1.2002246/t-005-1.2003490/a-083-1.2003519/a-083-1.2003520/ap-083-1.2003521?qr=1

  34. Harpaz R, Callahan A, Tamang S et al (2014) Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf 37(10):777–790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF (2008) Extracting information from textual documents in the electronic health record: a review of recent research. In: Yearbook of medical informatics, vol 35, pp 128–144

  36. Friedman C, Rindflesch TC, Corn M (2013) Natural language processing: state of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine. J Biomed Inform 46(5):765–773

    Article  PubMed  Google Scholar 

  37. Ford E, Carroll JA, Smith HE et al (2016) Extracting information from the text of electronic medical records to improve case detection: a systematic review. J Am Med Inform Assoc 23(5):1007–1015

    Article  PubMed  PubMed Central  Google Scholar 

  38. Perrillo RP, Martin P, Lok AS (2015) Preventing hepatitis B reactivation due to immunosuppressive drug treatments. JAMA 313(16):1617–1618

    Article  PubMed  Google Scholar 

  39. Yazdany J, Calabrese L (2010) Preventing hepatitis B reactivation in immunosuppressed patients: is it time to revisit the guidelines? Arthritis Care Res (Hoboken) 62(5):585–589

    Article  CAS  Google Scholar 

  40. Keane J, Gershon S, Wise RP et al (2001) Tuberculosis associated with infliximab, a tumor necrosis factor alpha-neutralizing agent. N Engl J Med 345(15):1098–1104

    Article  CAS  PubMed  Google Scholar 

  41. Carmona L, Hernandez-Garcia C, Vadillo C et al (2003) Increased risk of tuberculosis in patients with rheumatoid arthritis. J Rheumatol 30(7):1436–1439

    PubMed  Google Scholar 

  42. Clowse ME, Magder L, Petri M (2005) Cyclophosphamide for lupus during pregnancy. Lupus 14(8):593–597

    Article  CAS  PubMed  Google Scholar 

  43. Jackson P, Paquette L, Watiker V et al (2009) Intrauterine exposure to mycophenolate mofetil and multiple congenital anomalies in a newborn: possible teratogenic effect. Am J Med Genet Part A 149A(6):1231–1236

    Article  PubMed  Google Scholar 

  44. Yazdany J, Trupin L, Kaiser R et al (2011) Contraceptive counseling and use among women with systemic lupus erythematosus: a gap in health care quality? Arthritis Care Res (Hoboken) 63(3):358–365

    Google Scholar 

  45. Schwarz EB, Longo LS, Zhao X et al (2010) Provision of potentially teratogenic medications to female veterans of childbearing age. Med Care 48(9):834–842

    Article  PubMed  Google Scholar 

  46. Hwang JP, Somerfield MR, Alston-Johnson DE et al (2015) Hepatitis B virus screening for patients with cancer before therapy: american society of clinical oncology provisional clinical opinion update. J Clin Oncol 33(19):2212–2220

    Article  PubMed  PubMed Central  Google Scholar 

  47. Weinbaum CM, Mast EE, Ward JW (2009) Recommendations for identification and public health management of persons with chronic hepatitis B virus infection. Hepatology 49(5 Suppl):S35–S44

    Article  PubMed  Google Scholar 

  48. Reddy KR, Beavers KL, Hammond SP et al (2015) American gastroenterological association institute guideline on the prevention and treatment of hepatitis B virus reactivation during immunosuppressive drug therapy. Gastroenterology 148:215–219

    Article  CAS  PubMed  Google Scholar 

  49. Perrillo RP, Gish R, Falck-Ytter YT (2015) American Gastroenterological Association Institute technical review on prevention and treatment of hepatitis B virus reactivation during immunosuppressive drug therapy. Gastroenterology 148(1):221–244

    Article  PubMed  Google Scholar 

  50. Huang YH, Hsiao LT, Hong YC et al (2013) Randomized controlled trial of entecavir prophylaxis for rituximab-associated hepatitis B virus reactivation in patients with lymphoma and resolved hepatitis B. J Clin Oncol 31:2765–2772

    Article  CAS  PubMed  Google Scholar 

  51. Buti M, Morillas R, Manzano ML et al (2014) Tenofovir for the prophylaxis of HBV reactivation in anti-HBc-positive patients with hematologic malignancies treated with rituximab: preliminary results of a randomized study (PREBLIN Study). J Hepatol 60(1):S421–S422

    Article  Google Scholar 

  52. Kohler MJ, Amezaga M, Drozd J et al (2013) Use of a computerized order set to increase prescription of calcium and vitamin D supplementation in patients receiving glucocorticoids. J Gen Intern Med 28(6):825–829

    Article  PubMed  PubMed Central  Google Scholar 

  53. Chaudhry R, Schietel SM, North F et al (2013) Improving rates of herpes zoster vaccination with a clinical decision support system in a primary care practice. J Eval Clin Pract 19(2):263–266

    Article  PubMed  Google Scholar 

  54. van der Sijs H, Aarts J, Vulto A, Berg M (2006) Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 13(2):138–147 (Epub 2005 Dec 15)

    Article  PubMed  PubMed Central  Google Scholar 

  55. Suter LG, Barber CE, Herrin J et al (2016) American College of Rheumatology White Paper on performance outcome measures in rheumatology. Arthritis Care Res (Hoboken) 68(10):1390–1401

    Article  Google Scholar 

  56. Mannion R, Braithwaite J (2012) Unintended consequences of performance measurement in healthcare: 20 salutary lessons from the English National Health Service. Intern Med J 42(5):569–574

    Article  CAS  PubMed  Google Scholar 

  57. Ramsey CR, Matowe L, Grilli R et al (2003) Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behaviour change strategies. Int J Technol Assess Health Care 19(4):613–623

    Article  Google Scholar 

  58. Harris AD, McGregor JC, Perencevich EN et al (2006) The use and interpretation of quasi-experimental studies in medical informatics. J Am Med Inform Assoc 13(1):16–23 (Epub 2005 Oct 12)

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lewis RJ (2016) The pragmatic clinical trial in a learning health care system. Clin Trials 13(5):484–492

    Article  PubMed  Google Scholar 

  60. Mainous AG 3rd, Lambourne CA, Nietert PJ (2013) Impact of a clinical decision support system on antibiotic prescribing for acute respiratory infections in primary care: quasi-experimental trial. J Am Med Inform Assoc 20(2):317–324

    Article  PubMed  Google Scholar 

  61. Titsworth WL, Abram J, Guin P et al (2016) A prospective time-series quality improvement trial of a standardized analgesia protocol to reduce postoperative pain among neurosurgery patients. J Neurosurg 125(6):1523–1532

    Article  PubMed  Google Scholar 

  62. Green H, Paul M, Vidal L et al (2007) Prophylaxis of Pneumocystis pneumonia in immunocompromised non-HIV-infected patients: systematic review and meta-analysis of randomized controlled trials. Mayo Clin Proc 82(9):1052–1059

    Article  CAS  PubMed  Google Scholar 

  63. Jones RB, Tervaert JW, Hauser T et al (2010) Rituximab versus cyclophosphamide in ANCA-associated renal vasculitis. N Engl J Med 363(3):211–220

    Article  CAS  PubMed  Google Scholar 

  64. Singh JA, Furst DE, Bharat A et al (2012) 2012 Update of the 2008 American College of Rheumatology recommendations for the use of disease-modifying antirheumatic drugs and biologic agents in the treatment of rheumatoid arthritis. Arthritis Care Res (Hoboken) 64(5):625–639

    Article  CAS  Google Scholar 

  65. Bhatt DL, Scheiman J, Abraham NS et al (2008) ACCF/ACG/AHA 2008 expert consensus document on reducing the gastrointestinal risks of antiplatelet therapy and NSAID use. Am J Gastroenterol 103(11):2890

    Article  CAS  PubMed  Google Scholar 

  66. Saag KG, Teng GG, Patkar NM et al (2008) American College of Rheumatology 2008 recommendations for the use of nonbiologic and biologic disease-modifying antirheumatic drugs in rheumatoid arthritis. Arthritis Rheum 59(6):762–784

    Article  CAS  PubMed  Google Scholar 

  67. Shea B, Swinden MV, Tanjong Ghogomu E et al (2013) Folic acid and folinic acid for reducing side effects in patients receiving methotrexate for rheumatoid arthritis. Cochrane Database Syst Rev 41(6):1049–1060. doi:10.3899/jrheum.130738

  68. Marmor MF, Kellner U, Lai TY, American Academy of Ophthalmology et al (2016) Recommendations on screening for chloroquine and hydroxychloroquine retinopathy (2016 revision). Ophthalmology 2016(123):1386–1394

    Article  Google Scholar 

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Correspondence to Gabriela Schmajuk.

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Funding

This work is supported by the Agency for Healthcare and Research Quality [R01 HS024412] and the National Institutes of Health [K23 AR063770 (GS)]. Drs. Yazdany and Schmajuk are also supported by the Russell/Engleman Medical Research Center for Arthritis and an independent research grant from Pfizer. Dr. Yazdany is supported by the Robert L. Kroc Chair in Rheumatic and Connective Tissue Diseases (I). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality or National Institutes of Health.

Conflict of interest

Dr. Schmajuk has received an investigator initiated award from Pfizer. Dr. Yazdany has received an investigator initiated award from Pfizer.

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This article does not contain any studies with human participants performed by any of the authors.

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Schmajuk, G., Yazdany, J. Leveraging the electronic health record to improve quality and safety in rheumatology. Rheumatol Int 37, 1603–1610 (2017). https://doi.org/10.1007/s00296-017-3804-4

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