Use of health information technology to reduce diagnostic errors

BMJ Quality and Safety

Volumn 22, Issue SUPPL.2, 2013, Pages

  El Kareh, Robert ^a   Hasan, Omar ^b   Schiff, Gordon D ^c,d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b AMERICAN MEDICAL ASSOCIATION   (United States)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCESS TO INFORMATION; ALGORITHM; ASYMPTOMATIC DISEASE; COGNITION; COMPUTER AIDED DESIGN; CONCEPTUAL FRAMEWORK; DECISION SUPPORT SYSTEM; DIAGNOSTIC ERROR; DIFFERENTIAL DIAGNOSIS; ELECTRONIC MEDICAL RECORD; EVIDENCE BASED MEDICINE; FOLLOW UP; GOLD STANDARD; HUMAN; MEDICAL INFORMATICS; MEDICAL INFORMATION; META ANALYSIS; PATIENT INFORMATION; PHYSICAL EXAMINATION; PROGNOSIS; RADIOLOGY; REFERENCE DATABASE; REVIEW; SYSTEMATIC REVIEW; TAXONOMY; WORKFLOW;

CLINICAL DECISION SUPPORT SYSTEMS; CLINICAL INFORMATICS; DIAGNOSTIC ERRORS; HEALTH INFORMATION TECHNOLOGY; PATIENT SAFETY;

ALGORITHMS; DIAGNOSTIC ERRORS; HUMANS; MEDICAL INFORMATICS; PATIENT SAFETY; SAFETY MANAGEMENT;

EID: 84884566905     PISSN: 20445415     EISSN: None     Source Type: Journal    
DOI: 10.1136/bmjqs-2013-001884     Document Type: Review

References (152)

1. Schiff GD, Kim S, Abrams R, et al. Diagnosing Diagnosis Errors: Lessons from a Multi-institutional Collaborative Project and Methodology. 2005.
2. Berner ES, Graber ML. Overconfidence as a cause of diagnostic error in medicine. Am J Med 2008;121(5 Suppl): S2-23.
3. Newman-Toker DE, Pronovost PJ. Diagnostic errors: the next frontier for patient safety. JAMA 2009;301:1060-2.
4. Schwartz WB. Medicine and the computer. N Engl J Med 1970;283:1257-64.
5. Schwartz WB, Patil RS, Szolovits P. Artificial intelligence in medicine. Where do we stand? N Engl J Med 1987;316: 685-8.
6. Miller RA, Masarie FE. The demise of the Greek Oracle model for medical diagnostic systems. Methods Inf Med 1990;29:1-2.
7. Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science 1959;130:9-21.
8. Gorry G. Strategies for computer-aided diagnosis. Math Biosci 1968;2:293-318.
9. Gorry GA, Barnett GO. Experience with a model of sequential diagnosis. Comput Biomed Res 1968;1:490.
10. Spitzer RL, Endicott J. Diagno - a computer program for psychiatric diagnosis utilizing differential diagnostic procedure. Arch Gen Psychiatry 1968;18:746.
11. Shortliffe EH, Buchanan BG, Feigenbaum EA. Knowledge engineering for medical decision-making - review of computerbased clinical decision aids. Proc IEEE 1979;67:1207-24.
12. Rogers W, Ryack B, Moeller G. Computer-aided medical diagnosis - literature review. Int J Biomed Comput 1979;10:267-89.
13. Szolovits P, Pauker SG. Categorical and probabilistic reasoning in medical diagnosis. Artif Intell 1978;11:115-44.
14. Szolovits P, Patil RS, Schwartz WB. Artificial-intelligence in medical diagnosis. Ann Intern Med 1988;108:80-7.
15. Pauker SG, Gorry GA, Kassirer JP, et al. Towards simulation of clinical cognition - taking a present illness by computer. Am J Med 1976;60:981-96.
16. Shortliffe EH, Davis R, Axline SG, et al. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res 1975;8:303-20.
17. Miller RA, McNeil MA, Challinor SM, et al. The INTERNIST-1/QUICK MEDICAL REFERENCE project - status report. West J Med 1986;145:816-22.
18. Miller RA, Pople HE Jr., Myers JD. Internist-1, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 1982;307:468-76.
19. Warner H Jr, Warner HR, Haug P, et al. ILIAD as an expert consultant to teach differential diagnosis. Proceedings of the Annual Symposium on Computer Application in Medical Care; American Medical Informatics Association, 1988.
20. Barnett GO, Cimino JJ, Hupp JA, et al. DXplain. An evolving diagnostic decision-support system. JAMA 1987;258:67-74.
21. Miller RA. Medical diagnostic decision support systems - past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc 1994;1:8-27.
22. Johnston ME, Langton KB, Haynes RB, et al. Effects of computer-based clinical decision support systems on clinician performance and patient outcome. A critical appraisal of research. Ann Intern Med 1994;120:135-42.
23. Hunt DL, Haynes RB, Hanna SE, et al. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998;280:1339-46.
24. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293:1223-38.
25. Chase CR, Vacek PM, Shinozaki T, et al. Medical information management: improving the transfer of research results to presurgical evaluation. Med Care 1983;21:410-24.
26. Schiff GD, Hasan O, Kim S, et al. Diagnostic error in medicine: analysis of 583 physician-reported errors. Arch Intern Med 2009;169:1881-7.
27. Croskerry P. A universal model of diagnostic reasoning. Acad Med 2009;84:1022-8.
28. Klein GA. A recognition-primed decision (RPD) model of rapid decision making. In: Klein GA, Orasanu J, Calderwood R, et al. eds. Decision Making in Action: Models and Methods Norwood, NJ: Albex, 1993:138-47.
29. Hampton JR, Harrison MJ, Mitchell JR, et al. Relative contributions of history-taking, physical examination, and laboratory investigation to diagnosis and management of medical outpatients. Br Med J 1975;2:486-9.
30. Paley L, Zornitzki T, Cohen J, et al. Utility of clinical examination in the diagnosis of emergency department patients admitted to the department of medicine of an academic hospital. Arch Intern Med 2011;171:1394-6.
31. Sandler G. The importance of the history in the medical clinic and the cost of unnecessary tests. Am Heart J 1980;100(6 Pt 1): 928-31.
32. Slack WV, Hicks GP, Reed CE, et al. A computer-based medical-history system. N Engl J Med 1966;274:194-8.
33. Lucas RW, Card WI, Knill-Jones RP, et al. Computer interrogation of patients. Br Med J 1976;2:623-5.
34. Locke SE, Kowaloff HB, Hoff RG, et al. Computer-based interview for screening blood donors for risk of HIV transmission. JAMA 1992;268:1301-5.
35. Bachman JW. The patient-computer interview: a neglected tool that can aid the clinician. Mayo Clin Proc 2003;78:67-78.
36. Slack WV, Kowaloff HB, Davis RB, et al. Evaluation of computer-based medical histories taken by patients at home. J Am Med Inform Assoc 2012;19:545-8.
37. Benaroia M, Elinson R, Zarnke K. Patient-directed intelligent and interactive computer medical history-gathering systems: a utility and feasibility study in the emergency department. Int J Med Inform 2007;76:283-8.
38. Porter SC, Cai Z, Gribbons W, et al. The asthma kiosk: a patient-centered technology for collaborative decision support in the emergency department. J Am Med Inform Assoc 2004;11:458-67.
39. Adamson SC, Bachman JW. Pilot study of providing online care in a primary care setting. Mayo Clin Proc 2010;85:704-10.
40. Zakim D, Braun N, Fritz P, et al. Underutilization of information and knowledge in everyday medical practice: Evaluation of a computer-based solution. BMC Med Inform Decis Mak 2008;8.
41. Mahnke C. Automated heart sound analysis/computer-aided auscultation: a cardiologist's perspective and suggestions for future development. Conf Proc IEEE Eng Med Biol Soc 2009;2009:3115-18.
42. Thompson WR, Hayek CS, Tuchinda C, et al. Automated cardiac auscultation for detection of pathologic heart murmurs. Pediatr Cardiol 2001;22:373-9.
43. Pretorius E, Cronje ML, Strydom O. Development of a pediatric cardiac computer aided auscultation decision support system. Conf Proc IEEE Eng Med Biol Soc 2010; 2010:6078-82.
44. Abbas A, Fahim A. An automated computerized auscultation and diagnostic system for pulmonary diseases. J Med Syst 2010;34:1149-55.
45. Powsner SM, Tufte ER. Graphical summary of patient status. Lancet 1994;344:386-9.
46. Bauer DT, Guerlain S, Brown PJ. The design and evaluation of a graphical display for laboratory data. Neuron Glia Biol 2010;6:416-24.
47. Portet F, Reiter E, Gatt A, et al. Automatic generation of textual summaries from neonatal intensive care data. Artif Intell 2009;173:789-816.
48. Stead W. Healthcare culture in the United States. In: Grossmann C, Goolsby WA, Olsen LA, McGinnis JM, eds. Engineering a learning healthcare system: a look at the future: workshop summary. National Academies Press, 2011:120-5.
49. Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med 2003;78:775-80.
50. Ely JW, Graber ML, Croskerry P. Checklists to reduce diagnostic errors. Acad Med 2011;86:307-13.
51. Bond WF, Schwartz LM, Weaver KR, et al. Differential diagnosis generators: an evaluation of currently available computer programs. J Gen Intern Med 2012;27:213-19.
52. Ramnarayan P, Cronje N, Brown R, et al. Validation of a diagnostic reminder system in emergency medicine: a multi-centre study. Emerg Med J 2007;24:619-24.
53. Ramnarayan P, Tomlinson A, Rao A, et al. ISABEL: a web-based differential diagnostic aid for paediatrics: results from an initial performance evaluation. Arch Dis Child 2003;88:408-13.
54. Bavdekar SB, Pawar M. Evaluation of an Internet-delivered pediatric diagnosis support system (ISABEL(R)) in a tertiary care center in India. Indian Pediatr 2005;42:1086-91.
55. Thomas NJ, Ramnarayan P, Bell MJ, et al. An international assessment of a web-based diagnostic tool in critically ill children. Technol Health Care 2008;16:103-10.
56. Grober ML, Mathew A. Performance of a web-based clinical diagnosis support system for internists. J Gen Intern Med 2008;23:37-40.
57. Graber ML, Tompkins D, Holland JJ. Resources medical students use to derive a differential diagnosis. Med Teacher 2009;31:522-7.
58. Berner ES, Maisiak RS, Heuderbert GR, et al. Clinician performance and prominence of diagnoses displayed by a clinical diagnostic decision support system. Annual Symposium Proceedings/AMIA Symposium; 2003:76-80.
59. Ramnarayan P, Roberts GC, Coren M, et al. Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study. BMC Med Inform Decis Mak 2006;6:22.
60. Ramnarayan P, Kapoor RR, Coren M, et al. Measuring the impact of diagnostic decision support on the quality of clinical decision making: development of a reliable and valid composite score. J Am Med Inform Assoc 2003;10:563-72.
61. Ramnarayan P, Winrow A, Coren M, et al. Diagnostic omission errors in acute paediatric practice: impact of a reminder system on decision-making. BMC Med Inform Decis Mak 2006;6:37.
62. Graber M, VanScoy D. How well does decision support software perform in the emergency department? Emerg Med J 2003;20:426-8.
63. Kassirer JP, Kopelman RI. Cognitive errors in diagnosis: instantiation, classification, and consequences. Am J Med 1989;86:433-41.
64. Lemaire JB, Schaefer JP, Martin LA, et al. Effectiveness of the Quick Medical Reference as a diagnostic tool. CMAJ 1999;161:725-8.
65. Demeester RP, Bottieau E, Pini A, et al. Prospective multicenter evaluation of the expert system "KABISA TRAVEL" in diagnosing febrile illnesses occurring after a stay in the tropics. J Travel Med 2011;18:386-94.
66. Ebell M. AHRQ White Paper: use of clinical decision rules for point-of-care decision support. Med Decis Mak 2010;30:712-21.
67. Grady D, Berkowitz SA. Why is a good clinical prediction rule so hard to find? Arch Intern Med 2011;171:1701.
68. Runyon MS, Webb WB, Jones AE, et al. Comparison of the unstructured clinician estimate of pretest probability for pulmonary embolism to the Canadian score and the Charlotte rule: a prospective observational study. Acad Emerg Med 2005;12:587-93.
69. Douma RA, Mos IC, Erkens PM, et al. Performance of 4 clinical decision rules in the diagnostic management of acute pulmonary embolism: a prospective cohort study. Ann Intern Med 2011;154:709-18.
70. Oudega R, Moons K, Hoes AW. Ruling out deep venous thrombosis in primary care. A simple diagnostic algorithm including D-dimer testing. Thromb Haemost 2005;94:200-5.
71. Escriba A, Gamell AM, Fernandez Y, et al. Prospective validation of two systems of classification for the diagnosis of acute appendicitis. Pediatr Emerg Care 2011;27:165-9.
72. Baty V, Viel JF, Schuhmacher H, et al. Prospective validation of a diagnosis model as an aid to therapeutic decision-making in acute meningitis. Eur J Clin Microbiol Infect Dis 2000;19:422-6.
73. Nigrovic LE, Malley R, Kuppermann N. Meta-analysis of bacterial meningitis score validation studies. Arch Dis Child 2012;97:799-805.
74. Cohn KA, Thompson AD, Shah SS, et al. Validation of a clinical prediction rule to distinguish Lyme meningitis from aseptic meningitis. Pediatrics 2012;129:e46-53.
75. Stiell IG, Clement CM, Grimshaw J, et al. Implementation of the Canadian C-Spine Rule: prospective 12 centre cluster randomised trial. BMJ 2009;339:b4146.
76. Bandiera G, Stiell IG, Wells GA, et al. The Canadian C-spine rule performs better than unstructured physician judgment. Ann Emerg Med 2003;42:395-402.
77. Holmes JF, Wisner DH, McGahan JP, et al. Clinical prediction rules for identifying adults at very low risk for intraabdominal injuries after blunt trauma. Ann Emerg Med 2009;54:575-84.
78. Geusens P, Dumitrescu B, van Geel T, et al. Impact of systematic implementation of a clinical case finding strategy on diagnosis and therapy of postmenopausal osteoporosis. J Bone Miner Res 2008;23:812-18.
79. Aronsky D, Fiszman M, Chapman WW, et al. Combining decision support methodologies to diagnose pneumonia. J Am Med Inform Assoc 2001:12-16.
80. Schurink CAM, Visscher S, Lucas PJF, et al. A Bayesian decision-support system for diagnosing ventilator-associated pneumonia. Intensive Care Med 2007;33:1379-86.
81. Baxt WG, Shofer FS, Sites FD, et al. A neural computational aid to the diagnosis of acute myocardial infarction. Ann Emerg Med 2002;39:366-73.
82. Sakaguchi S, Takifuji K, Arita S, et al. Development of an early diagnostic system using fuzzy theory for postoperative infections in patients with gastric cancer. Dig Surg 2004;21:210-14.
83. Grigull L, Lechner WM. Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department. Pediatr Res 2012;71:725-31.
84. Hollander JE, Sease KL, Sparano DM, et al. Effects of neural network feedback to physicians on admit/discharge decision for emergency department patients with chest pain. Ann Emerg Med 2004;44:199-205.
85. Roy PM, Durieux P, Gillaizeau F, et al. A computerized handheld decision-support system to improve pulmonary embolism diagnosis a randomized trial. Ann Intern Med 2009;151:677-86.
86. Scheuermeyer FX, Innes G, Grafstein E, et al. Safety and efficiency of a chest pain diagnostic algorithm with selective outpatient stress testing for emergency department patients with potential ischemic chest pain. Ann Emerg Med 2012;59:256-64.e3.
87. English DR, Burton RC, del Mar CB, et al. Evaluation of aid to diagnosis of pigmented skin lesions in general practice: controlled trial randomised by practice. BMJ 2003;327:375.
88. Solberg LI, Wei F, Butler JC, et al. Effects of electronic decision support on high-tech diagnostic imaging orders and patients. Am J Manag Care 2010;16:102-6.
89. Curry L, Reed MH. Electronic decision support for diagnostic imaging in a primary care setting. J Am Med Inform Assoc 2011;18:267-70.
90. Cimino JJ. Use, usability, usefulness, and impact of an infobutton manager. AMIA Annu Symp Proc 2006:151-5.
91. Cimino JJ, Jing X, Del Fiol G. Meeting the electronic health record "Meaningful Use" criterion for the HL7 Infobutton standard using OpenInfobutton and the Librarian Infobutton Tailoring Environment (LITE). AMIA Annu Symp Proc 2012;2012:112-20.
92. Collins SA, Currie LM, Bakken S, et al. Information needs, Infobutton Manager use and satisfaction by clinician type: a case study. J Am Med Inform Assoc 2009;16:140-2.
93. Del Fiol G, Haug PJ, Cimino JJ, et al. Effectiveness of topic-specific Infobuttons: a randomized controlled trial. J Am Med Inform Assoc 2008;15:752-59.
94. Schiff GD.Minimizing diagnostic error: the importance of follow-up and feedback. Am J Med 2008;121(5 Suppl):S38-42.
95. Hoeksema LJ, Bazzy-Asaad A, Lomotan EA, et al. Accuracy of a computerized clinical decision-support system for asthma assessment and management. J Am Med Inform Assoc 2011;18:243-50.
96. Hsu W, Taira RK. Tools for improving the characterization and visualization of changes in neuro-oncology patients. AMIA Annu Symp Proc 2010;2010:316-20.
97. Willig JH, Krawitz M, Panjamapirom A, et al. Closing the feedback loop: an interactive voice response system to provide follow-up and feedback in primary care settings. J Med Syst 2013;37:9905.
98. Poon EG, Wang SJ, Gandhi TK, et al. Design and implementation of a comprehensive outpatient Results Manager. J Biomed Inform 2003;36:80-91.
99. Dalal AK, Poon EG, Karson AS, et al. Lessons learned from implementation of a computerized application for pending tests at hospital discharge. J Hosp Med 2011;6:16-21.
100. Dupuis EA, White HF, Newman D, et al. Tracking abnormal cervical cancer screening: evaluation of an EMR-based intervention. J Gen Intern Med 2010;25:575-80.
101. Verberne CJ, Nijboer CH, de Bock GH, et al. Evaluation of the use of decision-support software in carcino-embryonic antigen (CEA)-based follow-up of patients with colorectal cancer. BMC Med Inform Decis Mak 2012;12:14.
102. El-Kareh R, Roy C, Williams DH, et al. Impact of automated alerts on follow-up of post-discharge microbiology results: a cluster randomized controlled trial. J Gen Intern Med 2012;27:1243-50.
103. Humphrey LL, Shannon J, Partin MR, et al. Improving the follow-up of positive hemoccult screening tests: an electronic intervention. J Gen Intern Med 2011;26:691-7.
104. UK National Screening Committee. Programme appraisal criteria. http://www.screening.nhs.uk/criteria (accessed 3 Feb 2013)
105. Samson D, Schoelles KM. Chapter 2: Medical tests guidance (2) developing the topic and structuring systematic reviews of medical tests: utility of PICOTS, analytic frameworks, decision trees, and other frameworks. J Gen Intern Med 2012;27(Suppl 1):S11-19.
106. Smith RA, Cokkinides V, Brawley OW. Cancer screening in the United States, 2012. CA Cancer J Clin 2012;62:129-42.
107. Atlas SJ, Grant RW, Lester WT, et al. A cluster-randomized trial of a primary care informatics-based system for breast cancer screening. J Gen Intern Med 2011;26:154-61.
108. El-Kareh RE, Gandhi TK, Poon EG, et al. Actionable reminders did not improve performance over passive reminders for overdue tests in the primary care setting. J Am Med Inform Assoc 2011;18:160-3.
109. Leffler DA, Neeman N, Rabb JM, et al. An alerting system improves adherence to follow-up recommendations from colonoscopy examinations. Gastroenterology 2011;140: 1166.
110. Sequist TD, Zaslavsky AM, Marshall R, et al. Patient and physician reminders to promote colorectal cancer screening a randomized controlled trial. Arch Intern Med 2009;169: 364-71.
111. Nease DE, Ruffin MT, Klinkman MS, et al. Impact of a generalizable reminder system on colorectal cancer screening in diverse primary care practices: a report from the prompting and reminding at encounters for prevention project. Med Care 2008;46:S68-73.
112. Saleem JJ, Haggstrom DA, Militello LG, et al. Redesign of a computerized clinical reminder for colorectal cancer screening: a human-computer interaction evaluation. BMC Med Inform Decis Mak 2011;11:74.
113. Wagholikar KB, MacLaughlin KL, Henry MR, et al. Clinical decision support with automated text processing for cervical cancer screening. J Am Med Inform Assoc 2012;19: 833-9.
114. Rolnick SJ, Jackson JM, Amundson JH. Development, implementation and evaluation of an electronic medical record prompt for bone density testing. Health Inform J 2009;15:296-304.
115. Feldstein A, Elmer PJ, Smith DH, et al. Electronic medical record reminder improves osteoporosis management after a fracture: a randomized, controlled trial. J Am Geriatr Soc 2006;54:450-7.
116. DeJesus RS, Angstman KB, Kesman R, et al. Use of a clinical decision support system to increase osteoporosis screening. J Eval Clin Pract 2012;18:89-92.
117. Kenealy T, Arroll B, Petrie KJ. Patients and computers as reminders to screen for diabetes in family practice: randomized controlled trial. J Gen Intern Med 2005;20: 916-21.
118. Loo TS, Davis RB, Lipsitz LA, et al. Electronic medical record reminders and panel management to improve primary care of elderly patients. Arch Intern Med 2011;171:1552-8.
119. Steele AW, Eisert S, Davidson A, et al. Using computerized clinical decision support for latent tuberculosis infection screening. Am J Prev Med 2005;28:281-4.
120. Apkon M, Mattera JA, Lin ZQ, et al. A randomized outpatient trial of a decision-support information technology tool. Arch Intern Med 2005;165:2388-94.
121. Gill JM, Chen YX, Grimes A, et al. Using electronic health record-based tools to screen for bipolar disorder in primary care patients with depression. J Am Board Fam Med 2012;25:283-90.
122. Harinstein LM, Kane-Gill SL, Smithburger PL, et al. Use of an abnormal laboratory value-drug combination alert to detect drug-induced thrombocytopenia in critically ill patients. J Crit Care 2012;27:242-9.
123. Lee NJ, Chen ES, Currie LM, et al. The effect of a mobile clinical decision support system on the diagnosis of obesity and overweight in acute and primary care encounters. Adv Nurs Sci 2009;32:211-21.
124. Chaudhry R, Tulledge-Scheitel SM, Parks DA, et al. Use of a web-based clinical decision support system to improve abdominal aortic aneurysm screening in a primary care practice. J Eval Clin Pract 2012;18:666-70.
125. Walker J, Fairley CK, Walker SM, et al. Computer reminders for chlamydia screening in general practice: a randomized controlled trial. Sex Transm Dis 2010;37:445-50.
126. Lo HG, Matheny ME, Seger DL, et al. Impact of non-interruptive medication laboratory monitoring alerts in ambulatory care. J Am Med Inform Assoc 2009;16:66-71.
127. Schriefer SP, Landis SE, Turbow DJ, et al. Effect of a computerized body mass index prompt on diagnosis and treatment of adult obesity. Fam Med 2009;41:502-7.
128. Meurer WJ, Smith BL, Losman ED, et al. Real-time identification of serious infection in geriatric patients using clinical information system surveillance. J Am Geriatr Soc 2009;57:40-5.
129. Gance-Cleveland B, Gilbert LH, Kopanos T, et al. Evaluation of technology to identify and assess overweight children and adolescents. J Spec Pediatr Nurs 2010;15:72-83.
130. McIntosh N, Becher JC, Cunningham S, et al. Clinical diagnosis of pneumothorax is late: use of trend data and decision support might allow preclinical detection. Pediatr Res 2000;48:408-15.
131. Pasquale LR, Asefzadeh B, Dunphy RW, et al., Ocular TeleHealth Team. Detection of glaucoma-like optic discs in a diabetes teleretinal program. Optometry 2007;78:657-63.
132. Kesman RL, Rahman AS, Lin EY, et al. Population informatics-based system to improve osteoporosis screening in women in a primary care practice. J Am Med Inform Assoc 2010;17:212-16.
133. Warshaw EM, Hillman YJ, Greer NL, et al. Teledermatology for diagnosis and management of skin conditions: a systematic review. J Am Acad Dermatol 2011;64:759-72.
134. Eminovic N, de Keizer NF, Bindels PJ, et al. Maturity of teledermatology evaluation research: a systematic literature review. Br J Dermatol 2007;156:412-19.
135. Telemedicine and e-Health. http://www.liebertpub.com/TMJ (accessed 3 Feb 2013).
136. van der Heijden JP, Spuls PI, Voorbraak FP, et al. Tertiary teledermatology: a systematic review. Telemed J E Health 2010;16:56-62.
137. Armstrong AW, KwongMW, Chase EP, et al. Teledermatology operational considerations, challenges, and benefits: the referring providers' perspective. Telemed J E Health 2012;18:580-4.
138. Baumlin KM, Genes N, Landman A, et al. Electronic collaboration: using technology to solve old problems of quality care. Acad Emerg Med 2010;17:1312-21.
139. Delbanco T, Walker J, Bell SK, et al. Inviting patients to read their doctors' notes: a quasi-experimental study and a look ahead. Ann Intern Med 2012;157:461-70.
140. Delbanco T, Walker J, Darer JD, et al. Open notes: doctors and patients signing on. Ann Intern Med 2010;153:121-5.
141. Thomas KG, Thomas MR, Stroebel RJ, et al. Use of a registry-generated audit, feedback, and patient reminder intervention in an internal medicine resident clinic - a randomized trial. J Gen Intern Med 2007;22:1740-4.
142. Linder JA, Schnipper JL, Tsurikova R, et al. Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections. Am J Manag Care 2010;16(12 Suppl HIT):e311-19.
143. Roberts DH, Gilmartin GS, Neeman N, et al. Design and measurement of quality improvement indicators in ambulatory pulmonary care: creating a "culture of quality" in an academic pulmonary division. Chest 2009;136:1134-40.
144. Berner ES. Closing the Feedback Loop to Improve Diagnostic Quality: AHRQ, 2010.
145. Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003;10:523-30.
146. Sittig DF, Teich JM, Osheroff JA, et al. Improving clinical quality indicators through electronic health records: it takes more than just a reminder. Pediatrics 2009;124:375-7.
147. Lester WT, Zai AH, Grant RW, et al. Designing healthcare information technology to catalyse change in clinical care. Inform Prim Care 2008;16:9-19.
148. Schiff GD, Bates DW. Can electronic clinical documentation help prevent diagnostic errors? N Engl J Med 2010;362:1066-9.
149. Flanagan ME, Saleem JJ, Millitello LG, et al. Paper- and computer-based workarounds to electronic health record use at three benchmark institutions. J Am Med Inform Assoc 2013;20:e59-66.
150. Saleem JJ, Patterson ES, Militello L, et al. Using human factors methods to design a new interface for an electronic medical record. AMIA Annu Symp Proc 2007:640-4.
151. Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012;19:413-22.
152. Fryback DG, Thornbury Jr. The efficacy of diagnostic imaging. Med Decis Making 1991;11:88-94.