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Volumn 380, Issue 14, 2019, Pages 1347-1358

Machine learning in medicine

Author keywords

[No Author keywords available]

Indexed keywords

CANCER PROGNOSIS; CLINICAL DECISION MAKING; CLINICAL EVALUATION; CLINICAL PRACTICE; DATA PROCESSING; ELECTRONIC HEALTH RECORD; HEALTH CARE AVAILABILITY; HEALTH CARE QUALITY; HEALTH CARE SYSTEM; HUMAN; MACHINE LEARNING; MEDICAL DECISION MAKING; MELANOMA; PATIENT CARE; PRIORITY JOURNAL; RASH; REVIEW; SUPERVISED MACHINE LEARNING; TREATMENT PLANNING; TUMOR BIOPSY; TUMOR INVASION; WORKFLOW; DECISION SUPPORT SYSTEM; ELECTRONIC MEDICAL RECORD SYSTEM; MALE; MEDICAL INFORMATICS; MIDDLE AGED; SKIN TUMOR;

EID: 85063969463     PISSN: 00284793     EISSN: 15334406     Source Type: Journal    
DOI: 10.1056/NEJMra1814259     Document Type: Review
Times cited : (2072)

References (92)
  • 1
    • 85042102875 scopus 로고    scopus 로고
    • Redefining hypertension — Assessing the new blood-pressure guidelines
    • Bakris G, Sorrentino M. Redefining hypertension — assessing the new blood-pressure guidelines. N Engl J Med 2018; 378:497-9.
    • (2018) N Engl J Med , vol.378 , pp. 497-499
    • Bakris, G.1    Sorrentino, M.2
  • 3
    • 85063946074 scopus 로고    scopus 로고
    • stitute for Healthcare Improvement
    • Lasic M. Case study: an insulin overdose. Institute for Healthcare Improvement (http://www.ihi.org/education/IHIOpenSchool/resources/Pages/Activities/ AnInsulinOverdose.aspx).
    • Case Study: An Insulin Overdose
    • Lasic, M.1
  • 4
    • 0003413171 scopus 로고    scopus 로고
    • stitute of Medicine. Washington, DC: National Academies Press
    • Institute of Medicine. To err is human: building a safer health system. Washington, DC: National Academies Press, 2000.
    • (2000) To Err Is Human: Building A Safer Health System
  • 5
    • 85035814573 scopus 로고    scopus 로고
    • National Academies of Sciences, Engineering, and Medicine. Washington, DC: National Academies Press
    • National Academies of Sciences, Engineering, and Medicine. Improving diagnosis in health care. Washington, DC: National Academies Press, 2016.
    • (2016) Improving Diagnosis in Health Care
  • 6
    • 85050400028 scopus 로고    scopus 로고
    • How HIPAA harms care, and how to stop it
    • Berwick DM, Gaines ME. How HIPAA harms care, and how to stop it. JAMA 2018;320:229-30.
    • (2018) JAMA , vol.320 , pp. 229-230
    • Berwick, D.M.1    Gaines, M.E.2
  • 7
    • 85030466573 scopus 로고    scopus 로고
    • Lost in thought — The limits of the human mind and the future of medicine
    • Obermeyer Z, Lee TH. Lost in thought — the limits of the human mind and the future of medicine. N Engl J Med 2017; 377:1209-11.
    • (2017) N Engl J Med , vol.377 , pp. 1209-1211
    • Obermeyer, Z.1    Lee, T.H.2
  • 8
    • 0014931845 scopus 로고
    • Medicine and the computer — The promise and problems of change
    • Schwartz WB. Medicine and the computer — the promise and problems of change. N Engl J Med 1970;283:1257-64.
    • (1970) N Engl J Med , vol.283 , pp. 1257-1264
    • Schwartz, W.B.1
  • 9
    • 0023151888 scopus 로고
    • Artificial intelligence in medicine — Where do we stand?
    • Schwartz WB, Patil RS, Szolovits P. Artificial intelligence in medicine — where do we stand? N Engl J Med 1987; 316:685-8.
    • (1987) N Engl J Med , vol.316 , pp. 685-688
    • Schwartz, W.B.1    Patil, R.S.2    Szolovits, P.3
  • 11
    • 84898766195 scopus 로고    scopus 로고
    • Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations
    • Muntner P, Colantonio LD, Cushman M, et al. Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. JAMA 2014;311:1406-15.
    • (2014) JAMA , vol.311 , pp. 1406-1415
    • Muntner, P.1    Colantonio, L.D.2    Cushman, M.3
  • 12
    • 85025708722 scopus 로고    scopus 로고
    • Google turning its lucrative Web search over to AI machines
    • October 26
    • Clark J. Google turning its lucrative Web search over to AI machines. Bloom-berg News. October 26, 2015 (https://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web -search-over-to-ai-machines).
    • (2015) Bloom-Berg News
    • Clark, J.1
  • 15
    • 85054954639 scopus 로고    scopus 로고
    • Semi-supervised learning for information extraction from dialogue
    • Baixas, France: International Speech Communication Association
    • Kannan A, Chen K, Jaunzeikare D, Rajkomar A. Semi-supervised learning for information extraction from dialogue. In: Interspeech 2018. Baixas, France: International Speech Communication Association, 2018:2077-81.
    • (2018) Interspeech 2018 , pp. 2077-2081
    • Kannan, A.1    Chen, K.2    Jaunzeikare, D.3    Rajkomar, A.4
  • 17
    • 84994172098 scopus 로고    scopus 로고
    • Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals
    • Escobar GJ, Turk BJ, Ragins A, et al. Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals. J Hosp Med 2016; 11:Suppl 1:S18-S24.
    • (2016) J Hosp Med , vol.11 , pp. S18-S24
    • Escobar, G.J.1    Turk, B.J.2    Ragins, A.3
  • 18
    • 85054773608 scopus 로고    scopus 로고
    • Classification and personalized prognosis in myeloproliferative neoplasms
    • Grinfeld J, Nangalia J, Baxter EJ, et al. Classification and personalized prognosis in myeloproliferative neoplasms. N Engl J Med 2018;379:1416-30.
    • (2018) N Engl J Med , vol.379 , pp. 1416-1430
    • Grinfeld, J.1    Nangalia, J.2    Baxter, E.J.3
  • 19
    • 85059811921 scopus 로고    scopus 로고
    • High-performance medicine: The convergence of human and artificial intelligence
    • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25(1):44-56.
    • (2019) Nat Med , vol.25 , Issue.1 , pp. 44-56
    • Topol, E.J.1
  • 20
    • 85062259477 scopus 로고    scopus 로고
    • Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study
    • February 27 Epub ahead of print
    • Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019 February 27 (Epub ahead of print).
    • (2019) Gut
    • Wang, P.1    Berzin, T.M.2    Glissen Brown, J.R.3
  • 21
    • 85043470011 scopus 로고    scopus 로고
    • Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
    • Krause J, Gulshan V, Rahimy E, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 2018;125:1264-72.
    • (2018) Ophthalmology , vol.125 , pp. 1264-1272
    • Krause, J.1    Gulshan, V.2    Rahimy, E.3
  • 22
    • 85007529863 scopus 로고    scopus 로고
    • Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
    • Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-10.
    • (2016) JAMA , vol.316 , pp. 2402-2410
    • Gulshan, V.1    Peng, L.2    Coram, M.3
  • 23
    • 85038438910 scopus 로고    scopus 로고
    • Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes
    • Ting DSW, Cheung CY-L, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017;318:2211-23.
    • (2017) JAMA , vol.318 , pp. 2211-2223
    • Ting, D.S.W.1    Cheung, C.Y.-L.2    Lim, G.3
  • 24
    • 85042389905 scopus 로고    scopus 로고
    • Identifying medical diagnoses and treatable diseases by image-based deep learning
    • Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018;172(5):1122-1131.e9.
    • (2018) Cell , vol.172 , Issue.5
    • Kermany, D.S.1    Goldbaum, M.2    Cai, W.3
  • 25
    • 85042201755 scopus 로고    scopus 로고
    • Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
    • Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2018; 2:158-64.
    • (2018) Nat Biomed Eng , vol.2 , pp. 158-164
    • Poplin, R.1    Varadarajan, A.V.2    Blumer, K.3
  • 26
    • 85056358333 scopus 로고    scopus 로고
    • Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer
    • Steiner DF, MacDonald R, Liu Y, et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol 2018;42:1636-46.
    • (2018) Am J Surg Pathol , vol.42 , pp. 1636-1646
    • Steiner, D.F.1    MacDonald, R.2    Liu, Y.3
  • 27
    • 85061351913 scopus 로고    scopus 로고
    • Artificial intelligence-based breast cancer nodal metastasis detection
    • October 8 Epub ahead of print
    • Liu Y, Kohlberger T, Norouzi M, et al. Artificial intelligence-based breast cancer nodal metastasis detection. Arch Pathol Lab Med 2018 October 8 (Epub ahead of print).
    • (2018) Arch Pathol Lab Med
    • Liu, Y.1    Kohlberger, T.2    Norouzi, M.3
  • 28
    • 85038431889 scopus 로고    scopus 로고
    • Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
    • Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017; 318:2199-210.
    • (2017) JAMA , vol.318 , pp. 2199-2210
    • Ehteshami Bejnordi, B.1    Veta, M.2    Johannes van Diest, P.3
  • 29
    • 85058466258 scopus 로고    scopus 로고
    • Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study
    • Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018; 392:2388-96.
    • (2018) Lancet , vol.392 , pp. 2388-2396
    • Chilamkurthy, S.1    Ghosh, R.2    Tanamala, S.3
  • 30
    • 85053542469 scopus 로고    scopus 로고
    • Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: A prospective study
    • Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med 2018;169:357-66.
    • (2018) Ann Intern Med , vol.169 , pp. 357-366
    • Mori, Y.1    Kudo, S.E.2    Misawa, M.3
  • 31
    • 85045577663 scopus 로고    scopus 로고
    • Passive detection of atrial fibrillation using a commercially available smart-watch
    • Tison GH, Sanchez JM, Ballinger B, et al. Passive detection of atrial fibrillation using a commercially available smart-watch. JAMA Cardiol 2018;3:409-16.
    • (2018) JAMA Cardiol , vol.3 , pp. 409-416
    • Tison, G.H.1    Sanchez, J.M.2    Ballinger, B.3
  • 32
    • 85063023995 scopus 로고    scopus 로고
    • Non-invasive detection of hyperkalemia with a smartphone electrocardiogram and artificial intelligence
    • abstract
    • Galloway CD, Valys AV, Petterson FL, et al. Non-invasive detection of hyperkalemia with a smartphone electrocardiogram and artificial intelligence. J Am Coll Cardiol 2018;71:Suppl:A272. abstract.
    • (2018) J Am Coll Cardiol , vol.71 , pp. A272
    • Galloway, C.D.1    Valys, A.V.2    Petterson, F.L.3
  • 33
    • 85016143105 scopus 로고    scopus 로고
    • Dermatologist-level classification of skin cancer with deep neural networks
    • Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8.
    • (2017) Nature , vol.542 , pp. 115-118
    • Esteva, A.1    Kuprel, B.2    Novoa, R.A.3
  • 34
    • 85057246930 scopus 로고    scopus 로고
    • Weighting primary care patient panel size: A novel electronic health record-derived measure using machine learning
    • Rajkomar A, Yim JWL, Grumbach K, Parekh A. Weighting primary care patient panel size: a novel electronic health record-derived measure using machine learning. JMIR Med Inform 2016;4(4):e29.
    • (2016) JMIR Med Inform , vol.4 , Issue.4 , pp. e29
    • Rajkomar, A.1    Yim, J.W.L.2    Grumbach, K.3    Parekh, A.4
  • 35
    • 85031318320 scopus 로고    scopus 로고
    • Measuring the cost of quality measurement: A missing link in quality strategy
    • Schuster MA, Onorato SE, Meltzer DO. Measuring the cost of quality measurement: a missing link in quality strategy. JAMA 2017;318:1219-20.
    • (2017) JAMA , vol.318 , pp. 1219-1220
    • Schuster, M.A.1    Onorato, S.E.2    Meltzer, D.O.3
  • 36
    • 85044927780 scopus 로고    scopus 로고
    • Big data and machine learning in health care
    • Beam AL, Kohane IS. Big data and machine learning in health care. JAMA 2018;319:1317-8.
    • (2018) JAMA , vol.319 , pp. 1317-1318
    • Beam, A.L.1    Kohane, I.S.2
  • 38
    • 85053009378 scopus 로고    scopus 로고
    • Deep learning — A technology with the potential to transform health care
    • Hinton G. Deep learning — a technology with the potential to transform health care. JAMA 2018;320:1101-2.
    • (2018) JAMA , vol.320 , pp. 1101-1102
    • Hinton, G.1
  • 40
    • 84905990877 scopus 로고    scopus 로고
    • Big data in health care: Using analytics to identify and manage high-risk and high-cost patients
    • Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood) 2014;33:1123-31.
    • (2014) Health Aff (Millwood) , vol.33 , pp. 1123-1131
    • Bates, D.W.1    Saria, S.2    Ohno-Machado, L.3    Shah, A.4    Escobar, G.5
  • 41
    • 85127431078 scopus 로고    scopus 로고
    • Scalable and accurate deep learning with electronic health records
    • Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning with electronic health records. npj Digital Medicine 2018;1(1):18.
    • (2018) Npj Digital Medicine , vol.1 , Issue.1 , pp. 18
    • Rajkomar, A.1    Oren, E.2    Chen, K.3
  • 42
    • 85052522615 scopus 로고    scopus 로고
    • Clinically applicable deep learning for diagnosis and referral in retinal disease
    • De Fauw J, Ledsam JR, Romera-Pare-des B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342-50.
    • (2018) Nat Med , vol.24 , pp. 1342-1350
    • De Fauw, J.1    Ledsam, J.R.2    Romera-Pare-Des, B.3
  • 43
    • 0035798841 scopus 로고    scopus 로고
    • Public standards and patients’ control: How to keep electronic medical records accessible but private
    • Mandl KD, Szolovits P, Kohane IS. Public standards and patients’ control: how to keep electronic medical records accessible but private. BMJ 2001;322:283-7.
    • (2001) BMJ , vol.322 , pp. 283-287
    • Mandl, K.D.1    Szolovits, P.2    Kohane, I.S.3
  • 44
    • 84996564374 scopus 로고    scopus 로고
    • Time for a patient-driven health information economy?
    • Mandl KD, Kohane IS. Time for a patient-driven health information economy? N Engl J Med 2016;374:205-8.
    • (2016) N Engl J Med , vol.374 , pp. 205-208
    • Mandl, K.D.1    Kohane, I.S.2
  • 45
    • 84995784013 scopus 로고    scopus 로고
    • SMART on FHIR: A standards-based, interoperable apps platform for electronic health records
    • Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016;23:899-908.
    • (2016) J Am Med Inform Assoc , vol.23 , pp. 899-908
    • Mandel, J.C.1    Kreda, D.A.2    Mandl, K.D.3    Kohane, I.S.4    Ramoni, R.B.5
  • 46
    • 84879885267 scopus 로고    scopus 로고
    • Caveats for the use of operational electronic health record data in comparative effectiveness research
    • Hersh WR, Weiner MG, Embi PJ, et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care 2013;51: Suppl 3:S30-S37.
    • (2013) Med Care , vol.51 , pp. S30-S37
    • Hersh, W.R.1    Weiner, M.G.2    Embi, P.J.3
  • 47
    • 84950110026 scopus 로고    scopus 로고
    • Measurement is essential for improving diagnosis and reducing diagnostic error: A report from the Institute of Medicine
    • McGlynn EA, McDonald KM, Cassel CK. Measurement is essential for improving diagnosis and reducing diagnostic error: a report from the Institute of Medicine. JAMA 2015;314:2501-2.
    • (2015) JAMA , vol.314 , pp. 2501-2502
    • McGlynn, E.A.1    McDonald, K.M.2    Cassel, C.K.3
  • 48
    • 85035814573 scopus 로고    scopus 로고
    • stitute of Medicine, National Academies of Sciences, Engineering, and Medicine. Washington, DC: National Academies Press
    • Institute of Medicine, National Academies of Sciences, Engineering, and Medicine. Improving diagnosis in health care. Washington, DC: National Academies Press, 2016.
    • (2016) Improving Diagnosis in Health Care
  • 49
    • 85047299779 scopus 로고    scopus 로고
    • Rethinking assumptions about delivery of healthcare: Implications for universal health coverage
    • Das J, Woskie L, Rajbhandari R, Ab-basi K, Jha A. Rethinking assumptions about delivery of healthcare: implications for universal health coverage. BMJ 2018; 361:k1716.
    • (2018) BMJ , vol.361 , pp. k1716
    • Das, J.1    Woskie, L.2    Rajbhandari, R.3    Abbasi, K.4    Jha, A.5
  • 50
    • 70349869210 scopus 로고    scopus 로고
    • Longitudinal histories as predictors of future diagnoses of domestic abuse: Modelling study
    • Reis BY, Kohane IS, Mandl KD. Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study. BMJ 2009;339:b3677.
    • (2009) BMJ , vol.339 , pp. b3677
    • Reis, B.Y.1    Kohane, I.S.2    Mandl, K.D.3
  • 51
    • 85051571708 scopus 로고    scopus 로고
    • Overdiagnosis in primary care: Framing the problem and finding solutions
    • Kale MS, Korenstein D. Overdiagnosis in primary care: framing the problem and finding solutions. BMJ 2018;362:k2820.
    • (2018) BMJ , vol.362 , pp. k2820
    • Kale, M.S.1    Korenstein, D.2
  • 52
    • 84859406106 scopus 로고    scopus 로고
    • Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003-2009
    • Lindenauer PK, Lagu T, Shieh M-S, Pekow PS, Rothberg MB. Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003-2009. JAMA 2012;307: 1405-13.
    • (2012) JAMA , vol.307 , pp. 1405-1413
    • Lindenauer, P.K.1    Lagu, T.2    Shieh, M.-S.3    Pekow, P.S.4    Rothberg, M.B.5
  • 54
    • 84982175156 scopus 로고    scopus 로고
    • Pragmatic trials
    • Ford I, Norrie J. Pragmatic trials. N Engl J Med 2016;375:454-63.
    • (2016) N Engl J Med , vol.375 , pp. 454-463
    • Ford, I.1    Norrie, J.2
  • 55
    • 85026800348 scopus 로고    scopus 로고
    • Evidence for health decision making — Beyond randomized, controlled trials
    • Frieden TR. Evidence for health decision making — beyond randomized, controlled trials. N Engl J Med 2017;377:465-75.
    • (2017) N Engl J Med , vol.377 , pp. 465-475
    • Frieden, T.R.1
  • 57
    • 84973386990 scopus 로고    scopus 로고
    • Integrating randomized comparative effectiveness research with patient care
    • Fiore LD, Lavori PW. Integrating randomized comparative effectiveness research with patient care. N Engl J Med 2016;374:2152-8.
    • (2016) N Engl J Med , vol.374 , pp. 2152-2158
    • Fiore, L.D.1    Lavori, P.W.2
  • 58
    • 84901794133 scopus 로고    scopus 로고
    • Learning from big health care data
    • Schneeweiss S. Learning from big health care data. N Engl J Med 2014;370: 2161-3.
    • (2014) N Engl J Med , vol.370 , pp. 2161-2163
    • Schneeweiss, S.1
  • 59
    • 85063939094 scopus 로고    scopus 로고
    • stitute of Medicine. Washington, DC: National Academies Press
    • Institute of Medicine. The learning healthcare system: workshop summary. Washington, DC: National Academies Press, 2007.
    • (2007) The Learning Healthcare System: Workshop Summary
  • 60
    • 85020706563 scopus 로고    scopus 로고
    • Putting patients first by reducing administrative tasks in health care: A position paper of the American College of Physicians
    • Erickson SM, Rockwern B, Koltov M, McLean RM. Putting patients first by reducing administrative tasks in health care: a position paper of the American College of Physicians. Ann Intern Med 2017;166: 659-61.
    • (2017) Ann Intern Med , vol.166 , pp. 659-661
    • Erickson, S.M.1    Rockwern, B.2    Koltov, M.3    McLean, R.M.4
  • 61
    • 84887244957 scopus 로고    scopus 로고
    • 4000 Clicks: A productivity analysis of electronic medical records in a community hospital ED
    • Hill RG Jr, Sears LM, Melanson SW. 4000 Clicks: a productivity analysis of electronic medical records in a community hospital ED. Am J Emerg Med 2013; 31:1591-4.
    • (2013) Am J Emerg Med , vol.31 , pp. 1591-1594
    • Hill, R.G.1    Sears, L.M.2    Melanson, S.W.3
  • 62
    • 84940391901 scopus 로고    scopus 로고
    • Graphical display of diagnostic test results in electronic health records: A comparison of 8 systems
    • Sittig DF, Murphy DR, Smith MW, Russo E, Wright A, Singh H. Graphical display of diagnostic test results in electronic health records: a comparison of 8 systems. J Am Med Inform Assoc 2015; 22:900-4.
    • (2015) J Am Med Inform Assoc , vol.22 , pp. 900-904
    • Sittig, D.F.1    Murphy, D.R.2    Smith, M.W.3    Russo, E.4    Wright, A.5    Singh, H.6
  • 63
    • 84961944014 scopus 로고    scopus 로고
    • How do residents spend their shift time? A time and motion study with a particular focus on the use of computers
    • Mamykina L, Vawdrey DK, Hripcsak G. How do residents spend their shift time? A time and motion study with a particular focus on the use of computers. Acad Med 2016;91:827-32.
    • (2016) Acad Med , vol.91 , pp. 827-832
    • Mamykina, L.1    Vawdrey, D.K.2    Hripcsak, G.3
  • 64
    • 77949901712 scopus 로고    scopus 로고
    • Time spent on clinical documentation: A survey of internal medicine residents and program directors
    • Oxentenko AS, West CP, Popkave C, Weinberger SE, Kolars JC. Time spent on clinical documentation: a survey of internal medicine residents and program directors. Arch Intern Med 2010;170:377-80.
    • (2010) Arch Intern Med , vol.170 , pp. 377-380
    • Oxentenko, A.S.1    West, C.P.2    Popkave, C.3    Weinberger, S.E.4    Kolars, J.C.5
  • 65
    • 85029413665 scopus 로고    scopus 로고
    • Tethered to the EHR: Primary care physician workload assessment using EHR event log data and time-motion observations
    • Arndt BG, Beasley JW, Watkinson MD, et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017;15:419-26.
    • (2017) Ann Fam Med , vol.15 , pp. 419-426
    • Arndt, B.G.1    Beasley, J.W.2    Watkinson, M.D.3
  • 66
    • 85003038967 scopus 로고    scopus 로고
    • Allocation of physician time in ambulatory practice: A time and motion study in 4 specialties
    • Sinsky C, Colligan L, Li L, et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med 2016;165: 753-60.
    • (2016) Ann Intern Med , vol.165 , pp. 753-760
    • Sinsky, C.1    Colligan, L.2    Li, L.3
  • 67
    • 85044480242 scopus 로고    scopus 로고
    • Electronic health record usability issues and potential contribution to patient harm
    • Howe JL, Adams KT, Hettinger AZ, Ratwani RM. Electronic health record usability issues and potential contribution to patient harm. JAMA 2018;319:1276-8.
    • (2018) JAMA , vol.319 , pp. 1276-1278
    • Howe, J.L.1    Adams, K.T.2    Hettinger, A.Z.3    Ratwani, R.M.4
  • 68
    • 85042273587 scopus 로고    scopus 로고
    • Disentangling health care billing: For patients’ physical and financial health
    • Lee VS, Blanchfield BB. Disentangling health care billing: for patients’ physical and financial health. JAMA 2018;319:661-3.
    • (2018) JAMA , vol.319 , pp. 661-663
    • Lee, V.S.1    Blanchfield, B.B.2
  • 69
    • 59449089116 scopus 로고    scopus 로고
    • A surgical safety checklist to reduce morbidity and mortality in a global population
    • Haynes AB, Weiser TG, Berry WR, et al. A surgical safety checklist to reduce morbidity and mortality in a global population. N Engl J Med 2009;360:491-9.
    • (2009) N Engl J Med , vol.360 , pp. 491-499
    • Haynes, A.B.1    Weiser, T.G.2    Berry, W.R.3
  • 71
    • 85041018165 scopus 로고    scopus 로고
    • Exploring entertainment medicine and professionalization of self-care: Interview study among doctors on the potential effects of digital self-tracking
    • Gabriels K, Moerenhout T. Exploring entertainment medicine and professionalization of self-care: interview study among doctors on the potential effects of digital self-tracking. J Med Internet Res 2018;20(1):e10.
    • (2018) J Med Internet Res , vol.20 , Issue.1 , pp. e10
    • Gabriels, K.1    Moerenhout, T.2
  • 72
    • 85048773310 scopus 로고    scopus 로고
    • Association of a smartphone application with medication adherence and blood pressure control: The MedISAFE-BP randomized clinical trial
    • Morawski K, Ghazinouri R, Krumme A, et al. Association of a smartphone application with medication adherence and blood pressure control: the MedISAFE-BP randomized clinical trial. JAMA Intern Med 2018;178:802-9.
    • (2018) JAMA Intern Med , vol.178 , pp. 802-809
    • Morawski, K.1    Ghazinouri, R.2    Krumme, A.3
  • 73
    • 85023773744 scopus 로고    scopus 로고
    • Telemedi-cine for management of inflammatory bowel disease (myIBDcoach): A pragmatic, multicentre, randomised controlled trial
    • de Jong MJ, van der Meulen-de Jong AE, Romberg-Camps MJ, et al. Telemedi-cine for management of inflammatory bowel disease (myIBDcoach): a pragmatic, multicentre, randomised controlled trial. Lancet 2017;390:959-68.
    • (2017) Lancet , vol.390 , pp. 959-968
    • De Jong, M.J.1    Van Der Meulen-De Jong, A.E.2    Romberg-Camps, M.J.3
  • 74
    • 85060233746 scopus 로고    scopus 로고
    • Two-year survival comparing web-based symptom monitoring vs routine surveillance following treatment for lung cancer
    • Denis F, Basch E, Septans AL, et al. Two-year survival comparing web-based symptom monitoring vs routine surveillance following treatment for lung cancer. JAMA 2019;321(3):306-7.
    • (2019) JAMA , vol.321 , Issue.3 , pp. 306-307
    • Denis, F.1    Basch, E.2    Septans, A.L.3
  • 75
    • 85057555423 scopus 로고    scopus 로고
    • Safety of patient-facing digital symptom checkers
    • Fraser H, Coiera E, Wong D. Safety of patient-facing digital symptom checkers. Lancet 2018;392:2263-4.
    • (2018) Lancet , vol.392 , pp. 2263-2264
    • Fraser, H.1    Coiera, E.2    Wong, D.3
  • 76
    • 85021663116 scopus 로고    scopus 로고
    • Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: Observer accuracy and reproducibility study
    • Elmore JG, Barnhill RL, Elder DE, et al. Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study. BMJ 2017;357:j2813.
    • (2017) BMJ , vol.357 , pp. j2813
    • Elmore, J.G.1    Barnhill, R.L.2    Elder, D.E.3
  • 77
    • 85053019174 scopus 로고    scopus 로고
    • Potential biases in machine learning algorithms using electronic health record data
    • Gianfrancesco MA, Tamang S, Yaz-dany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018;178:1544-7.
    • (2018) JAMA Intern Med , vol.178 , pp. 1544-1547
    • Gianfrancesco, M.A.1    Tamang, S.2    Yazdany, J.3    Schmajuk, G.4
  • 80
    • 84999025038 scopus 로고    scopus 로고
    • Need for a national evaluation system for health technology
    • Shuren J, Califf RM. Need for a national evaluation system for health technology. JAMA 2016;316:1153-4.
    • (2016) JAMA , vol.316 , pp. 1153-1154
    • Shuren, J.1    Califf, R.M.2
  • 81
    • 84855698737 scopus 로고    scopus 로고
    • Clinical decision support systems could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation
    • Kesselheim AS, Cresswell K, Phansal-kar S, Bates DW, Sheikh A. Clinical decision support systems could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation. Health Aff (Millwood) 2011;30:2310-7.
    • (2011) Health Aff (Millwood) , vol.30 , pp. 2310-2317
    • Kesselheim, A.S.1    Cresswell, K.2    Phansalkar, S.3    Bates, D.W.4    Sheikh, A.5
  • 82
    • 85047308922 scopus 로고    scopus 로고
    • Balancing innovation and safety when integrating digital tools into health care
    • Auerbach AD, Neinstein A, Khanna R. Balancing innovation and safety when integrating digital tools into health care. Ann Intern Med 2018;168:733-4.
    • (2018) Ann Intern Med , vol.168 , pp. 733-734
    • Auerbach, A.D.1    Neinstein, A.2    Khanna, R.3
  • 83
    • 84905973448 scopus 로고    scopus 로고
    • Implementing electronic health care predictive analytics: Considerations and challenges
    • Amarasingham R, Patzer RE, Huesch M, Nguyen NQ, Xie B. Implementing electronic health care predictive analytics: considerations and challenges. Health Aff (Millwood) 2014;33:1148-54.
    • (2014) Health Aff (Millwood) , vol.33 , pp. 1148-1154
    • Amarasingham, R.1    Patzer, R.E.2    Huesch, M.3    Nguyen, N.Q.4    Xie, B.5
  • 84
    • 84937212736 scopus 로고    scopus 로고
    • The role of physicians in the era of predictive analytics
    • Sniderman AD, D’Agostino RB Sr, Pencina MJ. The role of physicians in the era of predictive analytics. JAMA 2015; 314:25-6.
    • (2015) JAMA , vol.314 , pp. 25-26
    • Sniderman, A.D.1    D’Agostino, R.B.2    Pencina, M.J.3
  • 85
    • 84905965765 scopus 로고    scopus 로고
    • Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system
    • Krumholz HM. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff (Millwood) 2014;33:1163-70.
    • (2014) Health Aff (Millwood) , vol.33 , pp. 1163-1170
    • Krumholz, H.M.1
  • 86
    • 85016430011 scopus 로고    scopus 로고
    • Automation bias and verification complexity: A systematic review
    • Lyell D, Coiera E. Automation bias and verification complexity: a systematic review. J Am Med Inform Assoc 2017;24: 423-31.
    • (2017) J Am Med Inform Assoc , vol.24 , pp. 423-431
    • Lyell, D.1    Coiera, E.2
  • 87
    • 85027869169 scopus 로고    scopus 로고
    • Unintended consequences of machine learning in medicine
    • Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA 2017;318:517-8.
    • (2017) JAMA , vol.318 , pp. 517-518
    • Cabitza, F.1    Rasoini, R.2    Gensini, G.F.3
  • 88
    • 84990235978 scopus 로고    scopus 로고
    • Can we open the black box of AI?
    • Castelvecchi D. Can we open the black box of AI? Nature 2016;538:20-3.
    • (2016) Nature , vol.538 , pp. 20-23
    • Castelvecchi, D.1
  • 89
    • 85063928915 scopus 로고    scopus 로고
    • To trust or not to trust a classifier
    • Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. New York: Curran Associates
    • Jiang H, Kim B, Guan M, Gupta M. To trust or not to trust a classifier. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Advances in neural information processing systems 31. New York: Curran Associates, 2018: 5541-52.
    • (2018) Advances in Neural Information Processing Systems , vol.31 , pp. 5541-5552
    • Jiang, H.1    Kim, B.2    Guan, M.3    Gupta, M.4
  • 90
    • 84905994854 scopus 로고    scopus 로고
    • The legal and ethical concerns that arise from using complex predictive analytics in health care
    • Cohen IG, Amarasingham R, Shah A, Xie B, Lo B. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff (Millwood) 2014;33:1139-47.
    • (2014) Health Aff (Millwood) , vol.33 , pp. 1139-1147
    • Cohen, I.G.1    Amarasingham, R.2    Shah, A.3    Xie, B.4    Lo, B.5
  • 91
    • 85063943981 scopus 로고    scopus 로고
    • Home
    • arXiv.org Home page (https://arxiv.org/).
  • 92
    • 85078471188 scopus 로고    scopus 로고
    • Copyright © 2019 Massachusetts Medical Society
    • bioRxiv. bioRxiv: The preprint server for biology (https://www.biorxiv.org/). Copyright © 2019 Massachusetts Medical Society.
    • bioRxiv: The Preprint Server for Biology


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