Big Data, Big Research

Recent trends in clinical research have moved attention toward reporting clinical outcomes and resource consumption associated with various care processes. This change is the result of technological advancement and a national effort to critically assess health care delivery. As orthopedic surgeons traverse an unchartered health care environment, a more complete understanding of how clinical research is conducted using large data sets is necessary. The purpose of this article is to review various advantages and disadvantages of large data sets available for orthopaedic use, examine their ideal use, and report how they are being implemented nationwide.

  • Use of the International Society of Arthroplasty Registries’ recommendations on creation of registries will enable orthopedic surgeons to target quality improvement initiatives and better track patient outcomes.

  • Use of the International Society of Arthroplasty Registries’ recommendations on creation of registries will enable orthopedic surgeons to target quality improvement initiatives and better track patient outcomes.

  • The Electronic Medical Record

    In the era of big data, new technologies are allowing orthopedic surgeons to collect and compute all patient data. The EMR will serve as the conduit between patient registries and clinicians, and as a result EMR functionality will be of utmost importance. The content and design of health care IT systems should be guided by the clinicians and not by administrators and their financially motivated concerns. The rigidity of medical records should give way to readily programmable and adaptable EMRs tailored to orthopedic surgeons. Lastly, EMRs should have a continuum capability in an effort to avoid the repopulation of previously collected data. As orthopedic surgeons attempt to create physician-owned medical facilities and ambulatory surgery centers, choice of EMR with such capabilities becomes a necessity.


    1. 1. Codman E.A.: The classic: the registry of bone sarcomas as an example of the end-result idea in hospital organization. 1924. Clin Orthop Relat Res 2009; 467: pp. 2766-2770

    2. 2. Goldstein J.: Private practice outcomes: validated outcomes data collection in private practice. Clin Orthop Relat Res 2010; 468: pp. 2640-2645

    3. 3. Arthur L. What is big data? 2013. Available at: Accessed November 12, 2015.

    4. 4. Nash D.B.: Harnessing the power of big data in healthcare. Am Health Drug Benefits 2014; 7: pp. 69-70

    5. 5. Grauer J.N., and Leopold S.S.: Editorial: large database studies–what they can do, what they cannot do, and which ones we will publish. Clin Orthop Relat Res 2015; 473: pp. 1537-1539

    6. 6. Stevens J.A., Ballesteros M.F., Mack K.A., et al: Gender differences in seeking care for falls in the aged Medicare population. Am J Prev Med 2012; 43: pp. 59-62

    7. 7. B U. Visualizing the big data industrial complex [Infographic]. 2013. Available at: Accessed November 12, 2015.

    8. 8. Groves P, Kayyali B, Knott D, et al. The big data revolution in healthcare. 2013. Available at: Accessed November 12, 2015.

    9. 9. Paxton E.W., Inacio M.C., Khatod M., et al: Kaiser Permanente National Total Joint Replacement Registry: aligning operations with information technology. Clin Orthop Relat Res 2010; 468: pp. 2646-2663

    10. 10. Pugely A.J., Martin C.T., Harwood J., et al: Research in orthopaedic surgery: part I: claims-based data. J Bone Joint Surg Am 2015; 97: pp. 1278-1287

    11. 11. Bohl D.D., Basques B.A., Golinvaux N.S., et al: Nationwide Inpatient Sample and National Surgical Quality Improvement Program give different results in hip fracture studies. Clin Orthop Relat Res 2014; 472: pp. 1672-1680

    12. 12. Helfet D.L., Hanson B.P., and De Faoite D.: Big data: the paradigm shift needed to revolutionize musculoskeletal clinical research. Am J Orthop 2014; 43: pp. 399-400

    13. 13. Robertsson O.: Knee arthroplasty registers. J Bone Joint Surg Br 2007; 89: pp. 1-4

    14. 14. Delaunay C.: Registries in orthopaedics. Orthop Traumatol Surg Res 2015; 101: pp. S69-S75

    15. 15. Havelin L.I., Robertsson O., Fenstad A.M., et al: Scandinavian experience of register collaboration: the Nordic Arthroplasty Register Association (NARA). J Bone Joint Surg Am 2011; 93: pp. 13-19

    16. 16. Marjoua Y., Butler C.A., and Bozic K.J.: Public reporting of cost and quality information in orthopaedics. Clin Orthop Relat Res 2012; 470: pp. 1017-1026

    17. 17. Khuri S.F., Daley J., Henderson W., et al: The Department of Veterans Affairs’ NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. National VA Surgical Quality Improvement Program. Ann Surg 1998; 228: pp. 491-507

    18. 18. Khuri S.F.: The NSQIP: a new frontier in surgery. Surgery 2005; 138: pp. 837-843

    19. 19. Fink A.S., Campbell D.A., Mentzer R.M., et al: The National Surgical Quality Improvement Program in non-Veterans Administration hospitals: initial demonstration of feasibility. Ann Surg 2002; 236: pp. 344-353

    20. 20. Jiranek W.A., Hanssen A.D., and Greenwald A.S.: Antibiotic-loaded bone cement for infection prophylaxis in total joint replacement. J Bone Joint Surg Am 2006; 88: pp. 2487-2500

    21. 21. Chiu F.Y., Chen C.M., Lin C.F., et al: Cefuroxime-impregnated cement in primary total knee arthroplasty: a prospective, randomized study of three hundred and forty knees. J Bone Joint Surg Am 2002; 84-A: pp. 759-762

    22. 22. Neuhaus V., King J., Hageman M.G., et al: Charlson comorbidity indices and in-hospital deaths in patients with hip fractures. Clin Orthop Relat Res 2013; 471: pp. 1712-1719

    23. 23. Karpanen T.J., Worthington T., Conway B.R., et al: Permeation of chlorhexidine from alcoholic and aqueous solutions within excised human skin. Antimicrob Agents Chemother 2009; 53: pp. 1717-1719

    24. 24. Bozic K.J., Bashyal R.K., Anthony S.G., et al: Is administratively coded comorbidity and complication data in total joint arthroplasty valid? Clin Orthop Relat Res 2013; 471: pp. 201-205

    25. 25. Malinzak R.A., Small S.R., Rogge R.D., et al: The effect of rotating platform TKA on strain distribution and torque transmission on the proximal tibia. J Arthroplasty 2014; 29: pp. 541-547

    26. 26. Malinzak R.A., Ritter M.A., Berend M.E., et al: Morbidly obese, diabetic, younger, and unilateral joint arthroplasty patients have elevated total joint arthroplasty infection rates. J Arthroplasty 2009; 24: pp. 84-88

    27. 27. D’Apuzzo M.R., Novicoff W.M., and Browne J.A.: The John Insall Award: morbid obesity independently impacts complications, mortality, and resource use after TKA. Clin Orthop Relat Res 2015; 473: pp. 57-63

    28. 28. Ladha K.S., Zhao K., Quraishi S.A., et al: The Deyo-Charlson and Elixhauser-van Walraven comorbidity indices as predictors of mortality in critically ill patients. BMJ Open 2015; 5: pp. e008990

    29. 29. Stanton T. OREF Award goes to SPORT project. AAOS Now 2014. Available at: Accessed October 8, 2015.

    30. 30. Maloney W.J.: National joint replacement registries: has the time come? J Bone Joint Surg Am 2001; 83-A: pp. 1582-1585

    31. 31. Belmont P.J., Goodman G.P., Kusnezov N.A., et al: Postoperative myocardial infarction and cardiac arrest following primary total knee and hip arthroplasty: rates, risk factors, and time of occurrence. J Bone Joint Surg Am 2014; 96: pp. 2025-2031

    32. 32. Bozic K.J., Kurtz S.M., Lau E., et al: The epidemiology of revision total knee arthroplasty in the United States. Clin Orthop Relat Res 2010; 468: pp. 45-51

    33. 33. Lansky D., and Milstein A.: Quality measurement in orthopaedics: the purchasers’ view. Clin Orthop Relat Res 2009; 467: pp. 2548-2555

    34. 34. Hauser D.L., Wessinger S.J., Condon R.T., et al: An electronic database for outcome studies that includes digital radiographs. J Arthroplasty 2001; 16: pp. 71-75

    35. 35. Malchau H., Garellick G., Eisler T., et al: Presidential guest address: the Swedish Hip Registry: increasing the sensitivity by patient outcome data. Clin Orthop Relat Res 2005; 441: pp. 19-29

    36. 36. Brinker M.R., and O’Connor D.P.: Stakeholders in outcome measures: review from a clinical perspective. Clin Orthop Relat Res 2013; 471: pp. 3426-3436

    37. 37. Saleh K.J., Bershadsky B., Cheng E., et al: Lessons learned from the hip and knee musculoskeletal outcomes data evaluation and management system. Clin Orthop Relat Res 2004; undefined: pp. 272-278

    38. 38. Franklin P.D., Lewallen D., Bozic K., et al: Implementation of patient-reported outcome measures in U.S. total joint replacement registries: rationale, status, and plans. J Bone Joint Surg Am 2014; 96: pp. 104-109

    39. 39. Bozic K.J., and Chiu V.: Quality measurement and public reporting in total joint arthroplasty. J Arthroplasty 2008; 23: pp. 146-149

    40. 40. Bozic K.J., Smith A.R., and Mauerhan D.R.: Pay-for-performance in orthopedics: implications for clinical practice. J Arthroplasty 2007; 22: pp. 8-12

    41. 41. O’Brien J.M., Corrigan J., Reitzner J.B., et al: Will performance measurement lead to better patient outcomes? What are the roles of the National Quality Forum and Medical Specialty Societies? Chest 2012; 141: pp. 300-307

    42. 42. Quality measures for public reporting: final recommendations to the Minnesota Department of Health. 2009. Available at: Accessed September 21, 2015.

    43. 43. Illinois hospital report card: where the data comes from. 2015. Available at: Accessed September 21, 2015.

    44. 44. Morrissey J.: Internet company rates hospitals. Mod Healthc 1999; 29: pp. 24-25

    45. 45. Bakhsh W., and Mesfin A.: Online ratings of orthopedic surgeons: analysis of 2185 reviews. Am J Orthop 2014; 43: pp. 359-363

    46. 46. Shively N. UCLA sued over recent hospital records hacking. 2015. Available at: Accessed September 3, 2015.

    47. 47. Wang Y.C., Hart D.L., Stratford P.W., et al: Clinical interpretation of computerized adaptive test-generated outcome measures in patients with knee impairments. Arch Phys Med Rehabil 2009; 90: pp. 1340-1348

    48. 48. Lee S.J., Kavanaugh A., and Lenert L.: Electronic and computer-generated patient questionnaires in standard care. Best practice & research. Clin Rheumatol 2007; 21: pp. 637-647

    49. 49. Larsson U.S., Svardsudd K., Wedel H., et al: Patient involvement in decision-making in surgical and orthopaedic practice: the Project Perioperative Risk. Soc Sci Med 1989; 28: pp. 829-835

    Only gold members can continue reading. Log In or Register to continue

    Feb 23, 2017 | Posted by in ORTHOPEDIC | Comments Off on Big Data, Big Research
    Premium Wordpress Themes by UFO Themes