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An interaction framework for supporting the adoption of EHRS by physicians

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Abstract

Using electronic health record systems (EHRSs) is essential to healthcare organizations to provide better services and an efficient use of resources. However, the adoption of EHRS is scarce, and contemporary literature only describes a characterization of the EHRS’ adoption problem without discussing how to support their adoption interactively. This paper sets forth an IT-based interaction framework to promote the adoption of EHRS. The establishing of four cognitive processes for interactively encouraging the adoption of EHRS by physicians, who are their most influential users, tailored the framework. This paper reports an approximation in this direction, describing the assessment of an interaction framework founded on adoption of innovations theory and constructed as an interactive system for facilitating the adoption of innovations in EHRS. The findings of this study indicate that an interactive system would give physicians a relative advantage when using an EHRS. As a result, interactive systems would provide the basis for supporting the adoption of EHRS by physicians for the benefit of stakeholders.

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References

  1. Castillo, V., Martinez-Garcia, A., Pulido, J.R.G.: A knowledge-based taxonomy of critical factors for adopting electronic health record systems by physicians: a systematic literature review. BMC Med. Inform. Decis. Mak. 10(1), 60 (2010)

    Article  Google Scholar 

  2. Menachemi, N., Collum, T.H.: Benefits and drawbacks of electronic health record systems. Risk Manag. Healthc. Policy 4, 47–55 (2011). https://doi.org/10.2147/RMHP.S12985

    Article  Google Scholar 

  3. Bowman, S.: Impact of electronic health record systems on information integrity: quality and safety implications. Perspect. Health Inf. Manag. 10(Fall), 1c (2013)

    Google Scholar 

  4. McCoy, A.B., Wright, A., Eysenbach, G., Malin, B.A., Patterson, E.S., Xu, H., Sittig, D.F.: State of the art in clinical informatics: evidence and examples. IMIA Yearbook 8, 13–19 (2013)

    Google Scholar 

  5. Jamoom, E.W., Patel, V., Furukawa, M.F., King, J.: EHR adopters vs non-adopters: impacts of, barriers to, and federal initiatives for EHR adoption. Healthcare 2(1), 33–39 (2014). https://doi.org/10.1016/j.hjdsi.2013.12.004

    Article  Google Scholar 

  6. WHO: Global Diffusion of eHealth: Making Universal Health Coverage Achievable Report of the Third Global Survey on eHealth. World Health Organization, Geneva (2016)

    Google Scholar 

  7. Asan, O.: Providers’ perceived facilitators and barriers to EHR screen sharing in outpatient settings. Appl. Ergon. 58, 301–307 (2017). https://doi.org/10.1016/j.apergo.2016.07.005

    Article  Google Scholar 

  8. Gagnon, M.-P., Simonyan, D., Ghandour, E.K., Godin, G., Labrecque, M., Ouimet, M., Rousseau, M.: Factors influencing electronic health record adoption by physicians: a multilevel analysis. Int. J. Inf. Manage. 36(3), 258–270 (2016). https://doi.org/10.1016/j.ijinfomgt.2015.12.002

    Article  Google Scholar 

  9. Chao, C.-A.: The impact of electronic health records on collaborative work routines: a narrative network analysis. Int. J. Med. Informatics 94, 100–111 (2016). https://doi.org/10.1016/j.ijmedinf.2016.06.019

    Article  Google Scholar 

  10. Rashotte, J., Varpio, L., Day, K., Kuziemsky, C., Parush, A., Elliott-Miller, P., King, J.W., Roffey, T.: Mapping communication spaces: the development and use of a tool for analyzing the impact of EHRs on interprofessional collaborative practice. Int. J. Med. Informatics 93, 2–13 (2016). https://doi.org/10.1016/j.ijmedinf.2016.05.003

    Article  Google Scholar 

  11. Pickering, B.W., Dong, Y., Ahmed, A., Giri, J., Kilickaya, O., Gupta, A., Gajic, O., Herasevich, V.: The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: a pilot step-wedge cluster randomized trial. Int. J. Med. Informatics 84(5), 299–307 (2015). https://doi.org/10.1016/j.ijmedinf.2015.01.017

    Article  Google Scholar 

  12. Mitchell, M., Hedt-Gauthier, B., Msellemu, D., Nkaka, M., Lesh, N.: Using electronic technology to improve clinical care - results from a before-after cluster trial to evaluate assessment and classification of sick children according to Integrated Management of Childhood Illness (IMCI) protocol in Tanzania. BMC Med. Inform. Decis. Mak. 13, 1472–6947 (2013). https://doi.org/10.1186/1472-6947-13-95

    Article  Google Scholar 

  13. Kruse, C.S., Kristof, C., Jones, B., Mitchell, E., Martinez, A.: Barriers to electronic health record adoption: a systematic literature review. J. Med. Syst. 40(12), 252 (2016). https://doi.org/10.1007/s10916-016-0628-9

    Article  Google Scholar 

  14. Vedel, I., Lapointe, L., Lussier, M.-T., Richard, C., Goudreau, J., Lalonde, L., Turcotte, A.: Healthcare professionals’ adoption and use of a clinical information system (CIS) in primary care: insights from the Da Vinci study. Int. J. Med. Informatics 81(2), 73–87 (2012). https://doi.org/10.1016/j.ijmedinf.2011.11.002

    Article  Google Scholar 

  15. Price, M.M., Pak, R., Müller, H., Stronge, A.: Older adults’ perceptions of usefulness of personal health records. Univ. Access Inf. Soc. (2013). https://doi.org/10.1007/s10209-012-0275-y

    Article  Google Scholar 

  16. Abdekhoda, M., Ahmadi, M., Gohari, M., Noruzi, A.: The effects of organizational contextual factors on physicians’ attitude toward adoption of Electronic Medical Records. J. Biomed. Inform. 53, 174–179 (2015). https://doi.org/10.1016/j.jbi.2014.10.008

    Article  Google Scholar 

  17. Rogers, E.: Diffusion of innovations, 5th edn. Free Press, New York (2003)

    Google Scholar 

  18. Davidson, S.M., Heineke, J.: Toward an effective strategy for the diffusion and use of clinical information systems. J. Am. Med. Inform. Assoc. 14(3), 361–367 (2007). https://doi.org/10.1197/jamia.M2254

    Article  Google Scholar 

  19. Poissant, L., Pereira, J., Tamblyn, R., Kawasumi, Y.: The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J. Am. Med. Inform. Assoc. 12(5), 505–516 (2005)

    Article  Google Scholar 

  20. Varpio, L., Rashotte, J., Day, K., King, J., Kuziemsky, C., Parush, A.: The EHR and building the patient’s story: a qualitative investigation of how EHR use obstructs a vital clinical activity. Int. J. Med. Inform. 84(12), 1019–1028 (2015). https://doi.org/10.1016/j.ijmedinf.2015.09.004

    Article  Google Scholar 

  21. Geibert, R.C.: Using diffusion of innovation concepts to enhance implementation of an electronic health record to support evidence-based practice. Nurs Adm Q (2006). https://doi.org/10.1097/00006216-200607000-00004

    Article  Google Scholar 

  22. Senathirajah, Y., Bakken, S., Kaufman, D.: The clinician in the driver’s seat: part 1—a drag/drop user-composable electronic health record platform. J. Biomed. Inform. 52, 165–176 (2014). https://doi.org/10.1016/j.jbi.2014.09.002

    Article  Google Scholar 

  23. Farri, O., Monsen, K.A., Pakhomov, S.V., Pieczkiewicz, D.S., Speedie, S.M., Melton, G.B.: Effects of time constraints on clinician–computer interaction: a study on information synthesis from EHR clinical notes. J. Biomed. Inform. 46(6), 1136–1144 (2013). https://doi.org/10.1016/j.jbi.2013.08.009

    Article  Google Scholar 

  24. Grinspan, Z.M., Banerjee, S., Kaushal, R., Kern, L.M.: Physician specialty and variations in adoption of electronic health records. Appl. Clin. Inform. 4(2), 225–240 (2013). https://doi.org/10.4338/ACI-2013-02-RA-0015

    Article  Google Scholar 

  25. Li, J.: A sociotechnical approach to evaluating the impact of ICT on clinical care environments. Open Med. Inform. J. 4, 202–205 (2010). https://doi.org/10.2174/1874431101004010202

    Article  Google Scholar 

  26. Bramble, J.D., Galt, K.A., Siracuse, M.V., Abbott, A.A., Drincic, A., Paschal, K.A., Fuji, K.T.: The relationship between physician practice characteristics and physician adoption of electronic health records. Health Care Manage. Rev. 35(1), 55–64 (2010)

    Article  Google Scholar 

  27. Grinspan, Z.M., Banerjee, S., Kaushal, R., Kern, L.M.: Physician specialty and variations in adoption of electronic health records. Appl. Clin. Inform. 4(2), 225–240 (2013). https://doi.org/10.4338/ACI-2013-02-RA-0015

    Article  Google Scholar 

  28. Ash, J.S., Bates, D.W.: Factors and forces affecting EHR system adoption: report of a 2004 ACMI discussion. J. Am. Med. Inform. Assoc. 12(1), 8–12 (2005)

    Article  Google Scholar 

  29. Johnson, M.: Literature review. Scaling agricultural technologies and innovation diffusion. United States Agency for International Development, Washington, DC (2015)

    Google Scholar 

  30. Rice, R.E.: Diffusion of innovations. Theoretical extensions. In: Nabi, R.L., Oliver, M.B. (eds.) The SAGE Handbook of Media Processes and Effects, pp. 489–503. Sage, Thousand Oaks (2015)

    Google Scholar 

  31. Heimly, V., Grimsmo, A., Faxvaag, A.: Diffusion of Electronic Health Records and electronic communication in Norway. Appl. Clin. Inform. 2(3), 355–364 (2011). https://doi.org/10.4338/ACI-2011-01-IE-0008

    Article  Google Scholar 

  32. Liebe, J.D., Hüsers, J., Hübner, U.: Investigating the roots of successful IT adoption processes–an empirical study exploring the shared awareness-knowledge of directors of nursing and chief information officers. BMC Med. Inform. Decis. Mak. 16(1), 10 (2016). https://doi.org/10.1186/s12911-016-0244-0

    Article  Google Scholar 

  33. Salahshour Rad, M., Nilashi, M., Mohamed Dahlan, H.: Information technology adoption: a review of the literature and classification. Univ. Access Inf. Soc. (2017). https://doi.org/10.1007/s10209-017-0534-z

    Article  Google Scholar 

  34. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 318–340 (1989)

    Article  Google Scholar 

  35. Holden, R.J., Karsh, B.-T.: The technology acceptance model: its past and its future in health care. J. Biomed. Inform. 43(1), 159–172 (2010). https://doi.org/10.1016/j.jbi.2009.07.002

    Article  Google Scholar 

  36. Li, J., Talaei-Khoei, A., Seale, H., Ray, P., MacIntyre, C.R.: Health care provider adoption of eHealth: systematic literature review. Interact. J. Med. Res. 2(1), e7 (2013). https://doi.org/10.2196/ijmr.2468

    Article  Google Scholar 

  37. Ducey, A.J., Coovert, M.D.: Predicting tablet computer use: an extended technology acceptance model for physicians. Health Policy Technol. 5(3), 268–284 (2016). https://doi.org/10.1016/j.hlpt.2016.03.010

    Article  Google Scholar 

  38. Gagnon, M.-P., Ghandour, E.K., Talla, P.K., Simonyan, D., Godin, G., Labrecque, M., Ouimet, M., Rousseau, M.: Electronic health record acceptance by physicians: testing an integrated theoretical model. J. Biomed. Inform. 48, 17–27 (2014). https://doi.org/10.1016/j.jbi.2013.10.010

    Article  Google Scholar 

  39. Iqbal, U., Ho, C.-H., Li, Y.-C., Nguyen, P.-A., Jian, W.-S., Wen, H.-C.: The relationship between usage intention and adoption of electronic health records at primary care clinics. Comput. Methods Programs Biomed. 112(3), 731–737 (2013). https://doi.org/10.1016/j.cmpb.2013.09.001

    Article  Google Scholar 

  40. Hamid, F., Cline, T.W.: Providers’ acceptance factors and their perceived barriers to electronic health record (EHR) adoption. Online J. Nurs. Inform. (OJNI) 17(3) (2013). http://ojni.org/issues/?p=2837

  41. Roda, C., Angehrn, A., Nabeth, T., Razmerita, L.: Using conversational agents to support the adoption of knowledge sharing practices. Interact. Comput. 15(1), 57 (2003)

    Article  Google Scholar 

  42. Alavi, M., Leidner, D.E.: Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q. 25(1), 107–136 (2001)

    Article  Google Scholar 

  43. Zheng, S., Rosson, M.B., Shih, P.C., Carroll, J.M.: Designing MOOCs as interactive places for collaborative learning. Paper presented at the Proceedings of the Second (2015) ACM Conference on Learning @ Scale, Vancouver, BC, Canada (2015)

  44. Schrenker, R.A.: Software engineering for future healthcare and clinical systems. Computer 39, 26–32 (2006)

    Article  Google Scholar 

  45. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)

    Article  Google Scholar 

  46. Humpage, S.D.: Benefits and costs of electronic medical records: the experience of mexico’s social security institute. The -American Development Bank Technical Notes. The Inter-American Development Bank, Washington, DC (2010)

    Google Scholar 

  47. R_Foundation: The R project for statistical computing (2016). https://www.r-project.org/

  48. Rosson, M.B., Carroll, J.M.: Usability Engineering: Scenario-Based Development of Human Computer Interaction, 1st edn. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  49. McHugh, M.L.: Interrater reliability: the kappa statistic. Biochem. Med. 22(3), 276–282 (2012)

    Article  MathSciNet  Google Scholar 

  50. Rochefort-Maranda, G.: Simplicity and model selection. Eur. J. Philos. Sci. 6(2), 261–279 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  51. Baker, A.: Simplicity. The Stanford Encyclopedia of Philosophy, Winter, vol. 2016. Metaphysics Research Lab, Stanford University, Stanford (2016)

    Google Scholar 

  52. Kelly, K.T.: How simplicity helps you find the truth without pointing at it. In: Friend, M., Goethe, N.B., Harizanov, V.S. (eds.) Induc, vol. 9, pp. 111–114. Springer, Berlin (2007)

    Google Scholar 

  53. Witten, I., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques Morgan Kaufmann Series in Data Management System, 4th edn. Morgan Kaufmann, Cambridge (2016)

    Google Scholar 

  54. Wixom, B.H., Todd, P.A.: A theoretical integration of user satisfaction and technology acceptance. Inf. Syst. Res. 16(1), 85–102 (2005)

    Article  Google Scholar 

  55. Carter, L., Bélanger, F.: The utilization of e-government services: citizen trust, innovation and acceptance factors. Info Syst. J. 15, 5–25 (2005)

    Article  Google Scholar 

  56. McKinney, V., Yoon, K., Zahedi, F.M.: The measurement of web-customer satisfaction: an expectation and disconfirmation approach. Inf. Syst. Res. (2002). https://doi.org/10.1287/isre.13.3.296.76

    Article  Google Scholar 

  57. Kankanhalli, A., Tan, B.C.Y., Wei, K.-K.: Contributing knowledge to electronic knowledge repositories: an empirical investigation. MIS Q. 29(1), 113–143 (2005)

    Article  Google Scholar 

  58. Vijayasarathy, L.R.: Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Inf. Manag. 41(6), 747–762 (2004). https://doi.org/10.1016/j.im.2003.08.011

    Article  Google Scholar 

  59. Kim, D.J., Ferrin, D.L., Rao, H.R.: A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support Syst. 44(2), 544–564 (2008). https://doi.org/10.1016/j.dss.2007.07.001

    Article  Google Scholar 

  60. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008). https://doi.org/10.1111/j.1540-5915.2008.00192.x

    Article  Google Scholar 

  61. Holzinger, A., Errath, M., Searle, G., Thurnher, B., Slany, W.: From extreme programming and usability engineering to extreme usability in software engineering education (XP + UE → XU). In: 29th Annual International Computer Software and Applications Conference (COMPSAC’05), 26–28 July 2005, Vol. 161, pp. 169–172 (2005). https://doi.org/10.1109/compsac.2005.80

  62. Ford, E.W., Menachemi, N., Phillips, M.T.: Predicting the adoption of electronic health records by physicians: when will health care be paperless? J. Am. Med. Inform. Assoc. 13(1), 106–112 (2006). https://doi.org/10.1197/jamia.M1913

    Article  Google Scholar 

  63. Ammenwerth, E., Iller, C., Mahler, C.: IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study. BMC Med. Inform. Decis. Mak. (2006). https://doi.org/10.1186/1472-6947-1186-1183

    Article  Google Scholar 

  64. Berner, E.S., Detmer, D.E., Simborg, D.: Will the wave finally break? A brief view of the adoption of electronic medical records in the United States. J. Am. Med. Inform. Assoc. 12(1), 3–7 (2005)

    Article  Google Scholar 

  65. Shah, N.R., Seger, A.C., Seger, D.L., Fiskio, J.M., Kuperman, G.J., Blumenfeld, B., Recklet, E.G., Bates, D.W., Gandhi, T.K.: Improving acceptance of computerized prescribing alerts in ambulatory care. J. Am. Med. Inform. Assoc. (2006). https://doi.org/10.1197/jamia.M1868

    Article  Google Scholar 

  66. Chismar, W.G., Wiley-Patton, S.: Does the extended technology acceptance model apply to physicians. Paper Presented at the Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS’03) (2003)

  67. Zheng, K., Padmana, R., Johnsona, M.P., Diamond, H.S.: Understanding technology adoption in clinical care: clinician adoption behavior of a point-of-care reminder system. Int. J. Med. Inform. 74(7–8), 535–543 (2005)

    Article  Google Scholar 

  68. Falotico, R., Quatto, P.: Fleiss’ kappa statistic without paradoxes. Q. Quant. 49(2), 463–470 (2015). https://doi.org/10.1007/s11135-014-0003-1

    Article  Google Scholar 

  69. Marasini, D., Quatto, P., Ripamonti, E.: Assessing the interrater agreement for ordinal data through weighted indexes. Stat. Methods Med. Res. 25(6), 2611–2633 (2014)

    Article  Google Scholar 

  70. Trivedi, G., Pham, P., Chapman, W.W., Hwa, R., Wiebe, J., Hochheiser, H.: NLPReViz: an interactive tool for natural language processing on clinical text. J. Am. Med. Inform. Assoc. (2017). https://doi.org/10.1093/jamia/ocx070

    Article  Google Scholar 

  71. Lin, Y.L., Guerguerian, A.-M., Tomasi, J., Laussen, P., Trbovich, P.: Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology. BMC Med. Inform. Decis. Mak. 17(1), 122 (2017). https://doi.org/10.1186/s12911-017-0520-7

    Article  Google Scholar 

  72. Holzinger, A., Searle, G., Wernbacher, M.: The effect of previous exposure to technology on acceptance and its importance in usability and accessibility engineering. Univ. Access Inf. Soc. 10, 245 (2011). https://doi.org/10.1007/s10209-010-0212-x

    Article  Google Scholar 

  73. Turner-Bowker, D.M., Saris-Baglama, R.N., Smith, K.J., DeRosa, M.A., Paulsen, C.A., Hogue, S.J.: Heuristic evaluation and usability testing of a computerized patient-reported outcomes survey for headache sufferers. Telemed. J. E-Health 17(1), 40–45 (2011)

    Article  Google Scholar 

  74. Georgsson, M., Staggers, N., Weir, C.: A modified user-oriented heuristic evaluation of a mobile health system for diabetes self-management support. Comput. Inform. Nurs. 34(2), 77–84 (2016)

    Article  Google Scholar 

  75. Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform. 3(2), 119–131 (2016). https://doi.org/10.1007/s40708-016-0042-6

    Article  Google Scholar 

  76. Sadasivam, R., Cutrona, S., Kinney, R., Marlin, B., Mazor, K., Lemon, S., Houston, T.: Collective-intelligence recommender systems: advancing computer tailoring for health behavior change into the 21st century. J. Med. Internet Res. 18(3), e42 (2016)

    Article  Google Scholar 

  77. Chung, J., Cho, I.: The need for academic electronic health record systems in nurse education. Nurse Educ. Today 54, 83–88 (2017). https://doi.org/10.1016/j.nedt.2017.04.018

    Article  Google Scholar 

  78. Sorensen, J., Campbell, L.: Curricular path to value: integrating an academic electronic health record. J. Nurs. Educ. 55(12), 716–719 (2016)

    Article  Google Scholar 

  79. Marangunić, N., Granić, A.: Technology acceptance model: a literature review from 1986 to 2013. Univ. Access Inf. Soc. 14(1), 81–95 (2015). https://doi.org/10.1007/s10209-014-0348-1

    Article  Google Scholar 

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This study was funded by CONACYT under Grant 196400.

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Castillo, V.H., Martínez-García, A.I., Soriano-Equigua, L. et al. An interaction framework for supporting the adoption of EHRS by physicians. Univ Access Inf Soc 18, 399–412 (2019). https://doi.org/10.1007/s10209-018-0612-x

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