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Annual Review of Public Health

Volume 40, 2019

The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization

Abstract

Electronic health records (EHRs) adoption has become nearly universal during the past decade. Academic research into the effects of EHRs has examined factors influencing adoption, clinical care benefits, financial and cost implications, and more. We provide an interdisciplinary overview and synthesis of this literature, drawing on work in public and population health, informatics, medicine, management information systems, and economics. We then chart paths forward for policy, practice, and research.

Keyword(s): adoption, clinical care quality, EHR, electronic health records, machine learning, medical costs
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2019-04-01
2024-04-19
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Literature Cited

  1. 1. 
    Adler-Milstein J, DesRoches CM, Kralovec P, Foster G, Worzala C et al. 2015. Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Aff 34:2174–80
     [Google Scholar]
  2. 2. 
    Adler-Milstein J, Jha AK 2014. No evidence found that hospitals are using new electronic health records to increase Medicare reimbursements. Health Aff 33:1271–77
     [Google Scholar]
  3. 3. 
    Adler-Milstein J, Jha AK 2017. HITECH Act drove large gains in hospital electronic health record adoption. Health Aff 36:1416–22
     [Google Scholar]
  4. 4. 
    Adler-Milstein J, Lin SC, Jha AK 2016. The number of health information exchange efforts is declining, leaving the viability of broad clinical data exchange uncertain. Health Aff 35:1278–85
     [Google Scholar]
  5. 5. 
    Adler-Milstein J, Pfeifer E 2017. Information blocking: Is it occurring and what policy strategies can address it?. Milbank Q 95:117–35
     [Google Scholar]
  6. 6. 
    Agha L 2014. The effects of health information technology on the costs and quality of medical care. J. Health Econ. 34:19–30
     [Google Scholar]
  7. 7. 
    Agrawal A, Gans J, Goldfarb A 2016. The simple economics of machine intelligence. Harvard Business Review Nov. 17. https://hbr.org/2016/11/the-simple-economics-of-machine-intelligence
  8. 8. 
    Angst CM, Agarwal R 2009. Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion. MIS Q 33:339–70
     [Google Scholar]
  9. 9. 
    Angst CM, Agarwal R, Sambamurthy V, Kelley K 2010. Social contagion and information technology diffusion: the adoption of electronic medical records in US hospitals. Manag. Sci. 56:1219–41
     [Google Scholar]
  10. 10. 
    Atasoy H, Chen P-Y, Ganju K 2017. The spillover effects of health IT investments on regional healthcare costs. Manag. Sci. 64:2473–972
     [Google Scholar]
  11. 11. 
    Ayabakan S, Bardhan I, Zheng ZE, Kirksey K 2017. The impact of health information sharing on duplicate testing. MIS Q 41:1083–103
     [Google Scholar]
  12. 12. 
    Balas EA, Boren SA, Brown GD, Ewigman BG, Mitchell JA, Perkoff GT 1996. Effect of physician profiling on utilization. Meta-analysis of randomized clinical trials. J. Gen. Intern. Med. 11:584–90
     [Google Scholar]
  13. 13. 
    Bates DW, Leape LL, Cullen DJ, Laird N, Petersen LA et al. 1998. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 280:1311–16
     [Google Scholar]
  14. 14. 
    Belloni A, Chernozhukov V, Hansen C 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
     [Google Scholar]
  15. 15. 
    Bennett P, Hardiker NR 2016. The use of computerized clinical decision support systems in emergency care: a substantive review of the literature. J. Am. Med. Inform. Assoc. 24:655–68
     [Google Scholar]
  16. 16. 
    Bergquist SL, Brooks GA, Keating NL, Landrum MB, Rose S 2017. Classifying lung cancer severity with ensemble machine learning in health care claims data Presented at Machine Learning for Healthcare Conference Boston: Aug. 18–19
  17. 17. 
    Brynjolfsson E, McAfee A 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies New York: Norton
  18. 18. 
    Buntin MB, Burke MF, Hoaglin MC, Blumenthal D 2011. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff 30:464–71
     [Google Scholar]
  19. 19. 
    Campanella P, Lovato E, Marone C, Fallacara L, Mancuso A et al. 2016. The impact of electronic health records on healthcare quality: a systematic review and meta-analysis. Eur. J. Public Health 26:60–64
     [Google Scholar]
  20. 20. 
    Chaudhry B, Wang J, Wu S, Maglione M, Mojica W et al. 2006. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann. Intern. Med. 144:742–52
     [Google Scholar]
  21. 21. 
    Coughlin SS, Williams LB, Hatzigeorgiou C 2017. A systematic review of studies of web portals for patients with diabetes mellitus. mHealth 3:23
     [Google Scholar]
  22. 22. 
    Cutler DM, Feldman NE, Horwitz JR 2005. US adoption of computerized physician order entry systems. Health Aff 24:1654–63
     [Google Scholar]
  23. 23. 
    DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG et al. 2008. Electronic health records in ambulatory care—a national survey of physicians. N. Engl. J. Med. 359:50–60
     [Google Scholar]
  24. 24. 
    Devaraj S, Kohli R 2000. Information technology payoff in the health-care industry: a longitudinal study. J. Manag. Inform. Syst. 16:41–67
     [Google Scholar]
  25. 25. 
    Devaraj S, Ow TT, Kohli R 2013. Examining the impact of information technology and patient flow on healthcare performance: a Theory of Swift and Even Flow (TSEF) perspective. J. Oper. Manag. 31:181–92
     [Google Scholar]
  26. 26. 
    DeVore SD, Figlioli K 2010. Lessons premier hospitals learned about implementing electronic health records. Health Aff 29:664–67
     [Google Scholar]
  27. 27. 
    Dranove D, Forman C, Goldfarb A, Greenstein S 2014. The trillion dollar conundrum: complementarities and health information technology. Am. Econ. J. Econ. Policy 6:239–70
     [Google Scholar]
  28. 28. 
    Dranove D, Garthwaite C, Li B, Ody C 2015. Investment subsidies and the adoption of electronic medical records in hospitals. J. Health Econ. 44:309–19
     [Google Scholar]
  29. 29. 
    Eden KB, Totten AM, Kassakian SZ, Gorman PN, McDonagh MS et al. 2016. Barriers and facilitators to exchanging health information: a systematic review. Int. J. Med. Inform. 88:44–51
     [Google Scholar]
  30. 30. 
    Edwards A, Hollin I, Barry J, Kachnowski S 2010. Barriers to cross-institutional health information exchange: a literature review. J. Healthc. Inform. Manag. 24:22–34
     [Google Scholar]
  31. 31. 
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM et al. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–18
     [Google Scholar]
  32. 32. 
    Feinstein AR, Horwitz RI 1997. Problems in the “evidence” of “evidence-based medicine.”. Am. J. Med. 103:529–35
     [Google Scholar]
  33. 33. 
    Ford EW, Menachemi N, Peterson LT, Huerta TR 2009. Resistance is futile: but it is slowing the pace of EHR adoption nonetheless. J. Am. Med. Inform. Assoc. 16:274–81
     [Google Scholar]
  34. 34. 
    Freedman S, Lin H, Prince J 2018. Information technology and patient health: analyzing outcomes, populations, and mechanisms. Am. J. Health Econ. 4:51–79
     [Google Scholar]
  35. 35. 
    Frisse ME, Johnson KB, Nian H, Davison CL, Gadd CS et al. 2011. The financial impact of health information exchange on emergency department care. J. Am. Med. Inform. Assoc. 19:328–33
     [Google Scholar]
  36. 36. 
    Fritz D, Ceschi A, Curkovic I, Huber M, Egbring M et al. 2012. Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients. Eur. J. Clin. Pharmacol. 68:1209–19
     [Google Scholar]
  37. 37. 
    Galson S, Simon G 2016. Real-world evidence to guide the approval and use of new treatments Discuss. Pap., Natl. Acad. Med. Washington, DC:
  38. 38. 
    Ganju KK, Atasoy H, Pavlou PA 2016. Do electronic medical record systems inflate Medicare reimbursements? Res. Pap. 16-008 Fox Sch. Bus., Temple Univ.
  39. 39. 
    Gold M, McLaughlin C 2016. Assessing HITECH implementation and lessons: 5 years later. Milbank Q 94:654–87
     [Google Scholar]
  40. 40. 
    Goldzweig CL, Orshansky G, Paige NM, Towfigh AA, Haggstrom DA et al. 2013. Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes: a systematic review. Ann. Intern. Med. 159:677–87
     [Google Scholar]
  41. 41. 
    Gowrisankaran G, Joiner KA, Lin J 2016. Does health IT adoption lead to better information or worse incentives NBER Work. Pap. 22873
  42. 42. 
    Greenwood B, Ganju K, Angst CM 2017. Just what the doctor ordered? Physician mobility after the adoption of electronic health records. Acad. Manag. Proc. 2017:11853
     [Google Scholar]
  43. 43. 
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–10
     [Google Scholar]
  44. 44. 
    Hefter Y, Madahar P, Eisen LA, Gong MN 2016. A time-motion study of ICU workflow and the impact of strain. Crit. Care Med. 44:1482–89
     [Google Scholar]
  45. 45. 
    Hillestad R, Bigelow J, Bower A, Girosi F, Meili R et al. 2005. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff 24:1103–17
     [Google Scholar]
  46. 46. 
    Hitt LM, Tambe P 2016. Health care information technology, work organization, and nursing home performance. ILR Rev 69:834–59
     [Google Scholar]
  47. 47. 
    Holmgren AJ, Patel V, Adler-Milstein J 2017. Progress in interoperability: measuring US hospitals’ engagement in sharing patient data. Health Aff 36:1820–27
     [Google Scholar]
  48. 48. 
    Hydari MZ, Telang R, Marella W 2017. Saving patient Ryan—Can advanced electronic medical records make patient care safer?. Manag. Sci. http://dx.doi.org/10.2139/ssrn.2503702
    [Crossref]
  49. 49. 
    Ilie V, Van Slyke C, Parikh MA, Courtney JF 2009. Paper versus electronic medical records: the effects of access on physicians' decisions to use complex information technologies. Decis. Sci. 40:213–41
     [Google Scholar]
  50. 50. 
    IOM (Inst. Med.). 2000. To Err Is Human: Building a Safer Health System Washington, DC: Natl. Acad. Press
  51. 51. 
    IOM (Inst. Med.). 2001. Crossing the Quality Chasm: A New Health System for the 21st Century Washington, DC: Natl. Acad. Press
  52. 52. 
    ISO (Int. Organ. Stan.). 2008. Electronic health record definition, scope, and context ISO/TC 215 Tech. Rep., Int. Organ. Stand. Geneva:
  53. 53. 
    Jarow JP, LaVange L, Woodcock J 2017. Multidimensional evidence generation and FDA regulatory decision making: defining and using “real-world” data. JAMA 318:703–4
     [Google Scholar]
  54. 54. 
    Jensen PB, Jensen LJ, Brunak S 2012. Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13:395–405
     [Google Scholar]
  55. 55. 
    Jha AK 2010. Meaningful use of electronic health records: the road ahead. JAMA 304:1709–10
     [Google Scholar]
  56. 56. 
    Jha AK, DesRoches CM, Campbell EG, Donelan K, Rao SR et al. 2009. Use of electronic health records in U.S. hospitals. N. Engl. J. Med. 360:1628–38
     [Google Scholar]
  57. 57. 
    Jha AK, DesRoches CM, Kralovec PD, Joshi MS 2010. A progress report on electronic health records in US hospitals. Health Aff 29:1951–57
     [Google Scholar]
  58. 58. 
    Jones DA, Shipman JP, Plaut DA, Selden CR 2010. Characteristics of personal health records: findings of the Medical Library Association/National Library of Medicine Joint Electronic Personal Health Record Task Force. J. Med. Libr. Assoc. 98:243–49
     [Google Scholar]
  59. 59. 
    Jones SS, Heaton PS, Rudin RS, Schneider EC 2012. Unraveling the IT productivity paradox—lessons for health care. N. Engl. J. Med. 366:2243–45
     [Google Scholar]
  60. 60. 
    Jones SS, Rudin RS, Perry T, Shekelle PG 2014. Health information technology: an updated systematic review with a focus on meaningful use. Ann. Intern. Med. 160:48–54
     [Google Scholar]
  61. 61. 
    Karlsson LO, Nilsson S, Bång M, Nilsson L, Charitakis E, Janzon M 2018. A clinical decision support tool for improving adherence to guidelines on anticoagulant therapy in patients with atrial fibrillation at risk of stroke: a cluster-randomized trial in a Swedish primary care setting (the CDS-AF study). PLOS Med 15:e1002528
     [Google Scholar]
  62. 62. 
    Kazley AS, Diana ML, Ford EW, Menachemi N 2012. Is electronic health record use associated with patient satisfaction in hospitals?. Health Care Manag. Rev. 37:23–30
     [Google Scholar]
  63. 63. 
    Kohli R, Devaraj S, Ow TT 2012. Does information technology investment influence a firm's market value? A case of non-publicly traded healthcare firms. MIS Q 36:1145–63
     [Google Scholar]
  64. 64. 
    Kudenchuk PJ, Maynard C, Martin JS, Wirkus M, Weaver WD 1996. Comparison of presentation, treatment, and outcome of acute myocardial infarction in men versus women (the Myocardial Infarction Triage and Intervention Registry). Am. J. Cardiol. 78:9–14
     [Google Scholar]
  65. 65. 
    Kuperman GJ, Teich JM, Gandhi TK, Bates DW 2001. Patient safety and computerized medication ordering at Brigham and Women's Hospital. Jt. Comm. J. Qual. Improv. 27:509–21
     [Google Scholar]
  66. 66. 
    Lammers EJ, Adler-Milstein J, Kocher KE 2014. Does health information exchange reduce redundant imaging? Evidence from emergency departments. Med. Care 52:227–34
     [Google Scholar]
  67. 67. 
    Lammers EJ, McLaughlin CG 2017. Meaningful use of electronic health records and Medicare expenditures: evidence from a panel data analysis of US health care markets, 2010–2013. Health Serv. Res. 52:1364–86
     [Google Scholar]
  68. 68. 
    Lammers EJ, McLaughlin CG, Barna M 2016. Physician EHR adoption and potentially preventable hospital admissions among Medicare beneficiaries: panel data evidence, 2010–2013. Health Serv. Res. 51:2056–75
     [Google Scholar]
  69. 69. 
    Lee J, McCullough JS, Town RJ 2013. The impact of health information technology on hospital productivity. RAND J. Econ. 44:545–68
     [Google Scholar]
  70. 70. 
    Li B 2014. Cracking the codes: Do electronic medical records facilitate hospital revenue enhancement? Work. Pap., Kellogg Sch. Manag., Northwest. Univ. Evanston, IL:
  71. 71. 
    Madabhushi A, Lee G 2016. Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33:170–75
     [Google Scholar]
  72. 72. 
    Manaktala S, Claypool SR 2017. Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality. J. Am. Med. Inform. Assoc. 24:88–95
     [Google Scholar]
  73. 73. 
    McCullough JM, Zimmerman FJ, Rodriguez HP, Bell DS, Torrens PR 2014. Impact of clinical decision support on receipt of antibiotic prescriptions for acute bronchitis and upper respiratory tract infection. J. Am. Med. Inform. Assoc. 21:1091–97
     [Google Scholar]
  74. 74. 
    McCullough JS, Casey M, Moscovice I, Prasad S 2010. The effect of health information technology on quality in US hospitals. Health Aff 29:647–54
     [Google Scholar]
  75. 75. 
    McCullough JS, Christianson J, Leerapan B 2013. Do electronic medical records improve diabetes quality in physician practices?. Am. J. Manag. Care 19:144–49
     [Google Scholar]
  76. 76. 
    McCullough JS, Parente ST, Town R 2016. Health information technology and patient outcomes: the role of information and labor coordination. RAND J. Econ. 47:207–36
     [Google Scholar]
  77. 77. 
    McEvoy D, Barnett ML, Sittig DF, Aaron S, Mehrotra A, Wright A 2018. Changes in hospital bond ratings after the transition to a new electronic health record. J. Am. Med. Inform. Assoc. 25:572–74
     [Google Scholar]
  78. 78. 
    McInnes DK, Shimada SL, Rao SR, Quill A, Duggal M et al. 2013. Personal health record use and its association with antiretroviral adherence: survey and medical record data from 1871 US veterans infected with HIV. AIDS Behav 17:3091–100
     [Google Scholar]
  79. 79. 
    Meisenberg BR, Wright RR, Brady-Copertino CJ 2014. Reduction in chemotherapy order errors with computerized physician order entry. J. Oncol. Pract. 10:e5–9
     [Google Scholar]
  80. 80. 
    Menachemi N, Burkhardt J, Shewchuk R, Burke D, Brooks RG 2006. Hospital information technology and positive financial performance: a different approach to finding an ROI. J. Healthc. Manag. 51:40–58
     [Google Scholar]
  81. 81. 
    Mennemeyer ST, Menachemi N, Rahurkar S, Ford EW 2016. Impact of the HITECH Act on physicians’ adoption of electronic health records. J. Am. Med. Inform. Assoc. 23:375–79
     [Google Scholar]
  82. 82. 
    Miller AR, Tucker C 2009. Privacy protection and technology diffusion: the case of electronic medical records. Manag. Sci. 55:1077–93
     [Google Scholar]
  83. 83. 
    Miller AR, Tucker CE 2011. Can health care information technology save babies?. J. Political Econ. 119:289–324
     [Google Scholar]
  84. 84. 
    Miller RH, Sim I 2004. Physicians’ use of electronic medical records: barriers and solutions. Health Aff 23:116–26
     [Google Scholar]
  85. 85. 
    Mullainathan S, Spiess J 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
     [Google Scholar]
  86. 86. 
    NPSF (Natl. Patient Saf. Found.). 2015. Free from Harm: Accelerating Patient Safety Improvement Fifteen Years After to Err Is Human Boston: NPSF
  87. 87. 
    Obermeyer Z, Emanuel EJ 2016. Predicting the future—big data, machine learning, and clinical medicine. N. Engl. J. Med. 375:1216
     [Google Scholar]
  88. 88. 
    Parente ST, McCullough JS 2009. Health information technology and patient safety: evidence from panel data. Health Aff 28:357–60
     [Google Scholar]
  89. 89. 
    Payne TH, Hoey PJ, Nichol P, Lovis C 2003. Preparation and use of preconstructed orders, order sets, and order menus in a computerized provider order entry system. J. Am. Med. Inform. Assoc. 10:322–29
     [Google Scholar]
  90. 90. 
    Pestotnik SL, Classen DC, Evans RS, Burke JP 1996. Implementing antibiotic practice guidelines through computer-assisted decision support: clinical and financial outcomes. Ann. Intern. Med. 124:884–90
     [Google Scholar]
  91. 91. 
    Prgomet M, Li L, Niazkhani Z, Georgiou A, Westbrook JI 2017. Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis. J. Am. Med. Inform. Assoc. 24:413–22
     [Google Scholar]
  92. 92. 
    Ransbotham S, Overby EM, Jernigan MC 2016. Electronic trace data and legal outcomes: the effect of electronic medical records on malpractice claim resolution time Res. Pap. 2016-052 Scheller Coll. Bus., Ga. Inst. Technol. Atlanta:
  93. 93. 
    Read-Brown S, Hribar MR, Reznick LG, Lombardi LH, Parikh M et al. 2017. Time requirements for electronic health record use in an academic ophthalmology center. JAMA Ophthalmol 135:1250–57
     [Google Scholar]
  94. 94. 
    Rose S 2016. A machine learning framework for plan payment risk adjustment. Health Serv. Res. 51:2358–74
     [Google Scholar]
  95. 95. 
    Ross SE, Schilling LM, Fernald DH, Davidson AJ, West DR 2010. Health information exchange in small-to-medium sized family medicine practices: motivators, barriers, and potential facilitators of adoption. Int. J. Med. Inform. 79:123–29
     [Google Scholar]
  96. 96. 
    Shah SD, Cifu AS 2018. From guideline to order set to patient harm. JAMA 319:1207–8
     [Google Scholar]
  97. 97. 
    Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, Gross T et al. 2016. Real-world evidence—what is it and what can it tell us?. N. Engl. J. Med. 375:2293–97
     [Google Scholar]
  98. 98. 
    Shulman R, Singer M, Goldstone J, Bellingan G 2005. Medication errors: a prospective cohort study of hand-written and computerised physician order entry in the intensive care unit. Crit. Care 9:R516–21
     [Google Scholar]
  99. 99. 
    Sidorov J 2006. It ain't necessarily so: the electronic health record and the unlikely prospect of reducing health care costs. Health Aff 25:1079–85
     [Google Scholar]
  100. 100. 
    Smithburger PL, Buckley MS, Bejian S, Burenheide K, Kane-Gill SL 2011. A critical evaluation of clinical decision support for the detection of drug–drug interactions. Expert Opin. Drug Saf. 10:871–82
     [Google Scholar]
  101. 101. 
    Song PH, McAlearney AS, Robbins J, McCullough JS 2011. Exploring the business case for ambulatory electronic health record system adoption. J. Healthc. Manag. 56:169–82
     [Google Scholar]
  102. 102. 
    Sönnichsen A, Trampisch US, Rieckert A, Piccoliori G, Vögele A et al. 2016. Polypharmacy in chronic diseases–Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support (PRIMA-eDS): study protocol for a randomized controlled trial. Trials 17:57
     [Google Scholar]
  103. 103. 
    Terrell KM, Perkins AJ, Hui SL, Callahan CM, Dexter PR, Miller DK 2010. Computerized decision support for medication dosing in renal insufficiency: a randomized, controlled trial. Ann. Emerg. Med. 56:623–29
     [Google Scholar]
  104. 104. 
    Van der Laan MJ, Rose S 2011. Targeted Learning: Causal Inference for Observational and Experimental Data New York: Springer Sci. Bus. Media
  105. 105. 
    Ventegodt S, Andersen NJ, Brom B, Acup D, Merrick J, DΕ Greydanus 2009. Evidence-based medicine: four fundamental problems with the randomized clinical trial (RCT) used to document chemical medicine. Int. J. Adolesc. Med. Health 21:485–96
     [Google Scholar]
  106. 106. 
    Vest JR 2009. Health information exchange and healthcare utilization. J. Med. Syst. 33:223–31
     [Google Scholar]
  107. 107. 
    Walker J, Pan E, Johnston D, Adler-Milstein J, Bates DW, Middleton B 2005. The value of health care information exchange and interoperability. Health Aff 24:W5
     [Google Scholar]
  108. 108. 
    Wells S, Furness S, Rafter N, Horn E, Whittaker R et al. 2008. Integrated electronic decision support increases cardiovascular disease risk assessment four fold in routine primary care practice. Eur. J. Cardiovasc. Prev. Rehabil. 15:173–78
     [Google Scholar]
  109. 109. 
    Wright A, Ai A, Ash J, Wiesen JF, Hickman T-TT et al. 2018. Clinical decision support alert malfunctions: analysis and empirically derived taxonomy. J. Am. Med. Inform. Assoc. 25:496–506
     [Google Scholar]
  110. 110. 
    Wright A, Poon EG, Wald J, Feblowitz J, Pang JE et al. 2012. Randomized controlled trial of health maintenance reminders provided directly to patients through an electronic PHR. J. Gen. Intern. Med. 27:85–92
     [Google Scholar]
  111. 111. 
    Wright A, Soran C, Jenter CA, Volk LA, Bates DW, Simon SR 2010. Physician attitudes toward health information exchange: results of a statewide survey. J. Am. Med. Inform. Assoc. 17:66–70
     [Google Scholar]
  112. 112. 
    Zhang Y, Padman R, Patel N 2015. Paving the COWpath: learning and visualizing clinical pathways from electronic health record data. J. Biomed. Inform. 58:186–97
     [Google Scholar]
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