Appl Clin Inform 2015; 06(04): 684-697
DOI: 10.4338/ACI-2015-06-RA-0068
Research Article
Schattauer GmbH

Uncovering Hospitalists’ Information Needs from Outside Healthcare Facilities in the Context of Health Information Exchange Using Association Rule Learning

D.A. Martinez
1   Johns Hopkins University, Emergency Medicine, Baltimore, MD, United States
,
E. Mora
2   Politecnico di Milano, Dipartimento di Ingegneria Gestionale, Milan, Italy
,
M. Gemmani
2   Politecnico di Milano, Dipartimento di Ingegneria Gestionale, Milan, Italy
,
J. Zayas-Castro
3   University of South Florida, Industrial and Management Systems Engineering, Tampa, FL, United States
› Institutsangaben
Weitere Informationen

Correspondence to:

Diego A. Martinez, Ph.D.
Emergency Medicine
Johns Hopkins University
5801 Smith Avenue
Dvis Building Suite 220
Baltimore, MD 21209

Publikationsverlauf

received: 19. Juni 2015

accepted in revised form: 01. Oktober 2015

Publikationsdatum:
19. Dezember 2017 (online)

 

Summary

Background: Important barriers to health information exchange (HIE) adoption are clinical work-flow disruptions and troubles with the system interface. Prior research suggests that HIE interfaces providing faster access to useful information may stimulate use and reduce barriers for adoption; however, little is known about informational needs of hospitalists.

Objective: To study the association between patient health problems and the type of information requested from outside healthcare providers by hospitalists of a tertiary care hospital.

Methods: We searched operational data associated with fax-based exchange of patient information (previous HIE implementation) between hospitalists of an internal medicine department in a large urban tertiary care hospital in Florida, and any other affiliated and unaffiliated healthcare provider. All hospitalizations from October 2011 to March 2014 were included in the search. Strong association rules between health problems and types of information requested during each hospitalization were discovered using Apriori algorithm, which were then validated by a team of hospitalists of the same department.

Results: Only 13.7% (2 089 out of 15 230) of the hospitalizations generated at least one request of patient information to other providers. The transactional data showed 20 strong association rules between specific health problems and types of information exist. Among the 20 rules, for example, abdominal pain, chest pain, and anaemia patients are highly likely to have medical records and outside imaging results requested. Other health conditions, prone to have records requested, were lower urinary tract infection and back pain patients.

Conclusions: The presented list of strong co-occurrence of health problems and types of information requested by hospitalists from outside healthcare providers not only informs the implementation and design of HIE, but also helps to target future research on the impact of having access to outside information for specific patient cohorts. Our data-driven approach helps to reduce the typical biases of qualitative research.


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Conflicts of interest

The authors declare that they have no conflicts of interest in this study.

  • References

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  • 2 Anderson G. Chronic Conditions: Making the Case for Ongoing Care. Princeton, NJ: Robert Wood Johnson Foundation; 2010
  • 3 Lucas DJ, Ejaz A, Haut ER, Spolverato G, Haider AH, Pawlik TM. Interhospital transfer and adverse outcomes after general surgery: implications for pay for performance. J Am Coll Surg 2014; 218 (03) 393-400.
  • 4 Kho AN, Lemmon L, Commiskey M, Wilson SJ, McDonald CJ. Use of a Regional Health Information Exchange to Detect Crossover of Patients with MRSA between Urban Hospitals. J Am Med Informatics Assoc 2008; 15 (02) 212-216.
  • 5 Bailey JE, Wan JY, Mabry LM, Landy SH, Pope RA, Waters TM, Frisse ME. Does health information exchange reduce unnecessary neuroimaging and improve quality of headache care in the emergency department?. J Gen Intern Med 2013; 28 (02) 176-183.
  • 6 Bailey JE, Pope RA, Elliott EC, Wan JY, Waters TM, Frisse ME. Health Information Exchange Reduces Repeated Diagnostic Imaging for Back Pain. Ann Emerg Med 2013; 62 (01) 16-24.
  • 7 Unertl KM, Johnson KB, Lorenzi NM. Health information exchange technology on the front lines of healthcare: workflow factors and patterns of use. J Am Med Inform Assoc 2012; 19 (03) 392-400.
  • 8 Carr CM, Krywko DM, Moore HE, Saef SH. The Impact of a Health Information Exchange on the Management of Patients in an Urban Academic Emergency Department: An Observational Study and Cost Analysis. Ann Emerg Med 2012; 60 (04) S15.
  • 9 Vest JR, Miller TR. The association between health information exchange and measures of patient satisfaction. Appl Clin Inform 2011; 2 (04) 447-459.
  • 10 Solberg D, Roberts J. “Pipe dream” HIE proves challenging. A community hospital network, concerned that each clinic’s needs could not be entirely met, decided on a standard EHR platform and a shared community network. Health Manag Technol 2009; 30 (07) 22-23 30.
  • 11 Furukawa MF, King J, Patel V, Hsiao C-J, Adler-Milstein J, Jha AK. Despite Substantial Progress In EHR Adoption, Health Information Exchange And Patient Engagement Remain Low In Office Settings. Health Aff (Millwood) 2014; 33 (09) 1672-1679.
  • 12 Adler-Milstein J, Jha AK. Health information exchange among U. S. hospitals: Who’s in, who’s out, and why?. Healthc (Amst) 2014; 2 (01) 26-32.
  • 13 Rudin RS, Motala A, Goldzweig CL, Shekelle PG. Usage and Effect of Health Information Exchange: A Systematic Review. Ann Intern Med 2014; 161 (11) 803-812.
  • 14 Richardson JE, Abramson EL, Kaushal R. The value of health information exchange. J Healthc Leadersh 2012; 4: 17-23.
  • 15 Vest JR, Jasperson ’ S, Zhao H, Gamm LD, Ohsfeldt RL. Use of a health information exchange system in the emergency care of children. BMC Med Inform Decis Mak 2011; 11: 78.
  • 16 Hincapie AL, Warholak TL, Murcko AC, Slack M, Malone DC. Physicians’ opinions of a health information exchange. J Am Med Inform Assoc 2011; 18 (01) 60-65.
  • 17 Kassirer JP. Doctor discontent. N Engl J Med 1998; 339 (21) 1543-1545.
  • 18 Fischman J. Who will take care of you?. US News World Rep 2005; 138 (04) 44-46.
  • 19 Mechanic D. Physician discontent: challenges and opportunities. JAMA 2003; 290 (07) 941-946.
  • 20 Morrison I, Smith R. Hamster health care. BMJ 2000; 321 7276 1541-1542.
  • 21 Morrison I. The Future of Physicians’ Time. Ann Intern Med 2000; 132 (01) 80-84.
  • 22 Trude S. So much to do, so little time: physician capacity constraints, 1997–2001. Track Rep 2003; 8: 1-4.
  • 23 Sicotte C, Paré G. Success in health information exchange projects: Solving the implementation puzzle. Soc Sci Med 2010; 70: 1159-1165.
  • 24 Karsh B-T. Clinical Practice Improvement and Redesign: How Change in Workflow Can Be Supported by Clinical Decision Support. Rockville, Maryland: Agency for Healthcare Research and Quality; 2009: 1-34.
  • 25 Stead WW, Lin HS. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington (DC): National Academies Press (US); 2009: 121
  • 26 Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA 2007; 297 (08) 831-841.
  • 27 Stross JK, Harlan WR. The dissemination of new medical information. JAMA 1979; 241 (24) 2622-2624.
  • 28 Strasser TC. The information needs of practicing physicians in Northeastern New York State. J Med Libr Assoc 2012; 100 (Suppl. 04) G.
  • 29 Curry L, Putnam RW. Continuing medical education in Maritime Canada: The methods physicians use, would prefer and find most effective. Can Med Assoc J 1981; 124 (05) 563-566.
  • 30 Cohen SJ, Weinberger M, Mazzuca S, McDonald CJ. Perceived influence of different information sources on the decision-making of internal medicine house staff and faculty. Soc Sci Med 1982; 16 (14) 1361-1364.
  • 31 Northup DE, Moore-West M, Skipper B, Teaf SR. Characteristics of Clinical Information-Searching: Investigation Using Critical Incident Technique. J Med Educ 1983; 58 (11) 873-881.
  • 32 Kochen M, Cohen L, Wulff Y. Information systems and clinical research by residents in internal medicine. Methods Inf Med 1985; 24 (02) 85-90.
  • 33 Stinson ER, Mueller DA. Survey of health professionals’ information habits and needs. Conducted through personal interviews. JAMA 1980; 243 (02) 140-143.
  • 34 Christensen DB, Wertheimer AI. Sources of information and influence on new drug prescribing among physicians in an HMO. Soc Sci Med 1979; 13A (03) 313-322.
  • 35 Covell DG, Uman GC, Manning PR. Information needs in office practice: are they being met?. Ann Intern Med 1985; 103 (04) 596-599.
  • 36 Rudin R, Volk L, Simon S, Bates D. What Affects Clinicians’ Usage of Health Information Exchange?. Appl Clin Inform 2011; 2 (03) 250-262.
  • 37 Abdullah U, Ahmad J, Ahmed A. Analysis of effectiveness of apriori algorithm in medical billing data mining. Proceedings –4th IEEE International Conference on Emerging Technologies. 2008 ICET 2008: 327-331.
  • 38 Sharma N, Om H. Extracting Significant Patterns for Oral Cancer Detection Using Apriori Algorithm. Intell Inf Manag 2014; 6: 30-37.
  • 39 Ilayaraja M, Meyyappan T. Mining medical data to identify frequent diseases using Apriori algorithm. Proceedings of the 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, PRIME 2013: 194-199.
  • 40 Vest JR, Zhao H, Jasperson J, Jaspserson J, Gamm LD, Ohsfeldt RL. Factors motivating and affecting health information exchange usage. J Am Med Inform Assoc 2011; 18 (02) 143-149.
  • 41 Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. ACM SIGMOD Rec 1993; 22: 207-216.
  • 42 Hahsler M, Buchta BG, Hornik K. Arules: Mining Association Rules and Frequent item sets. 2015. https://cran.r-project.org/web/packages/arules/index.html .
  • 43 Alvarez SA. Chi-squared computation for association rules: preliminary results. Boston, MA: Boston College; 2003
  • 44 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995; 57 (01) 289-300.
  • 45 Smith K. Effective communication with primary care providers. Pediatr Clin North Am 2014; 61 (04) 671-679.
  • 46 Koopman RJ, Steege LMB, Moore JL, Clarke MA, Canfield SM, Kim MS, Belden JL. Physician Information Needs and Electronic Health Records (EHRs): Time to Reengineer the Clinic Note. J Am Board Fam Med 2015; 28 (03) 316-323.
  • 47 Landrigan CP, Conway PH, Edwards S, Srivastava R. Pediatric hospitalists: a systematic review of the literature. Pediatrics 2006; 117 (05) 1736-1744.
  • 48 Stewart BA, Fernandes S, Rodriguez-Huertas E, Landzberg M. A preliminary look at duplicate testing associated with lack of electronic health record interoperability for transferred patients. J Am Med Inform Assoc 2010; 17 (03) 341-344.
  • 49 Pantilat SZ, Lindenauer PK, Katz PP, Wachter RM. Primary care physician attitudes regarding communication with hospitalists. Am J Med 2001; 111 9B 15S-20S.
  • 50 Schabetsberger T, Ammenwerth E, Andreatta S, Gratl G, Haux R, Lechleitner G, Schindelwig K, Stark C, Vogl R, Wilhelmy I, Wozak F. From a paper-based transmission of discharge summaries to electronic communication in health care regions. Int J Med Inform 2006; 75 3–4 209-215.
  • 51 Laye PG. Tying up loose ends. Thermochim Acta 1997; 300 (01) 237-245.
  • 52 El-Kareh R, Roy C, Brodsky G, Perencevich M, Poon EG. Incidence and predictors of microbiology results returning postdischarge and requiring follow-up. J Hosp Med 2011; 6 (05) 291-296.
  • 53 Roy CL, Poon EC, Karson AS, Ladak-Merchant Z, Johnson RE, Maviglia SM, Gandhi TK. Patient safety concerns arising from test results that return after hospital discharge. Ann Intern Med 2005; 143 (02) 121-128.
  • 54 Walz SE, Smith M, Cox E, Sattin J, Kind AJH. Pending laboratory tests and the hospital discharge summary in patients discharged to sub-acute care. J Gen Intern Med 2011; 26 (04) 393-398.
  • 55 Van Walraven C, Laupacis A, Seth R, Wells G. Dictated versus database-generated discharge summaries: A randomized clinical trial. CMAJ 1999; 160 (03) 319-326.
  • 56 Teach RL, Shortliffe EH. An analysis of physician attitudes regarding computer-based clinical consultation systems. Comput Biomed Res 1981; 14 (06) 542-558.
  • 57 Singer J, Sacks HS, Lucente F, Chalmers TC. Physician attitudes toward applications of computer data base systems. JAMA 1983; 249 (12) 1610-1614.
  • 58 Campion TR, Edwards AM, Johnson SB, Kaushal R. Health information exchange system usage patterns in three communities: practice sites, users, patients, and data. Int J Med Inform 2013; 82 (Suppl. 09) 810-820.
  • 59 Johnson KB, Unertl KM, Chen Q, Lorenzi NM, Nian H, Bailey J, Frisse M. Health information exchange usage in emergency departments and clinics: the who, what, and why. J Am Med Inform Assoc 2011; 18 (05) 690-697.
  • 60 Ozkaynak M, Brennan PF. Revisiting sociotechnical systems in a case of unreported use of health information exchange system in three hospital emergency departments. J Eval Clin Pract 2013; 19 (02) 370-373.

Correspondence to:

Diego A. Martinez, Ph.D.
Emergency Medicine
Johns Hopkins University
5801 Smith Avenue
Dvis Building Suite 220
Baltimore, MD 21209

  • References

  • 1 Ward BW, Schiller JS, Goodman RA. Multiple chronic conditions among US adults: a 2012 update. Prev Chronic Dis 2014; 11: E62.
  • 2 Anderson G. Chronic Conditions: Making the Case for Ongoing Care. Princeton, NJ: Robert Wood Johnson Foundation; 2010
  • 3 Lucas DJ, Ejaz A, Haut ER, Spolverato G, Haider AH, Pawlik TM. Interhospital transfer and adverse outcomes after general surgery: implications for pay for performance. J Am Coll Surg 2014; 218 (03) 393-400.
  • 4 Kho AN, Lemmon L, Commiskey M, Wilson SJ, McDonald CJ. Use of a Regional Health Information Exchange to Detect Crossover of Patients with MRSA between Urban Hospitals. J Am Med Informatics Assoc 2008; 15 (02) 212-216.
  • 5 Bailey JE, Wan JY, Mabry LM, Landy SH, Pope RA, Waters TM, Frisse ME. Does health information exchange reduce unnecessary neuroimaging and improve quality of headache care in the emergency department?. J Gen Intern Med 2013; 28 (02) 176-183.
  • 6 Bailey JE, Pope RA, Elliott EC, Wan JY, Waters TM, Frisse ME. Health Information Exchange Reduces Repeated Diagnostic Imaging for Back Pain. Ann Emerg Med 2013; 62 (01) 16-24.
  • 7 Unertl KM, Johnson KB, Lorenzi NM. Health information exchange technology on the front lines of healthcare: workflow factors and patterns of use. J Am Med Inform Assoc 2012; 19 (03) 392-400.
  • 8 Carr CM, Krywko DM, Moore HE, Saef SH. The Impact of a Health Information Exchange on the Management of Patients in an Urban Academic Emergency Department: An Observational Study and Cost Analysis. Ann Emerg Med 2012; 60 (04) S15.
  • 9 Vest JR, Miller TR. The association between health information exchange and measures of patient satisfaction. Appl Clin Inform 2011; 2 (04) 447-459.
  • 10 Solberg D, Roberts J. “Pipe dream” HIE proves challenging. A community hospital network, concerned that each clinic’s needs could not be entirely met, decided on a standard EHR platform and a shared community network. Health Manag Technol 2009; 30 (07) 22-23 30.
  • 11 Furukawa MF, King J, Patel V, Hsiao C-J, Adler-Milstein J, Jha AK. Despite Substantial Progress In EHR Adoption, Health Information Exchange And Patient Engagement Remain Low In Office Settings. Health Aff (Millwood) 2014; 33 (09) 1672-1679.
  • 12 Adler-Milstein J, Jha AK. Health information exchange among U. S. hospitals: Who’s in, who’s out, and why?. Healthc (Amst) 2014; 2 (01) 26-32.
  • 13 Rudin RS, Motala A, Goldzweig CL, Shekelle PG. Usage and Effect of Health Information Exchange: A Systematic Review. Ann Intern Med 2014; 161 (11) 803-812.
  • 14 Richardson JE, Abramson EL, Kaushal R. The value of health information exchange. J Healthc Leadersh 2012; 4: 17-23.
  • 15 Vest JR, Jasperson ’ S, Zhao H, Gamm LD, Ohsfeldt RL. Use of a health information exchange system in the emergency care of children. BMC Med Inform Decis Mak 2011; 11: 78.
  • 16 Hincapie AL, Warholak TL, Murcko AC, Slack M, Malone DC. Physicians’ opinions of a health information exchange. J Am Med Inform Assoc 2011; 18 (01) 60-65.
  • 17 Kassirer JP. Doctor discontent. N Engl J Med 1998; 339 (21) 1543-1545.
  • 18 Fischman J. Who will take care of you?. US News World Rep 2005; 138 (04) 44-46.
  • 19 Mechanic D. Physician discontent: challenges and opportunities. JAMA 2003; 290 (07) 941-946.
  • 20 Morrison I, Smith R. Hamster health care. BMJ 2000; 321 7276 1541-1542.
  • 21 Morrison I. The Future of Physicians’ Time. Ann Intern Med 2000; 132 (01) 80-84.
  • 22 Trude S. So much to do, so little time: physician capacity constraints, 1997–2001. Track Rep 2003; 8: 1-4.
  • 23 Sicotte C, Paré G. Success in health information exchange projects: Solving the implementation puzzle. Soc Sci Med 2010; 70: 1159-1165.
  • 24 Karsh B-T. Clinical Practice Improvement and Redesign: How Change in Workflow Can Be Supported by Clinical Decision Support. Rockville, Maryland: Agency for Healthcare Research and Quality; 2009: 1-34.
  • 25 Stead WW, Lin HS. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington (DC): National Academies Press (US); 2009: 121
  • 26 Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA 2007; 297 (08) 831-841.
  • 27 Stross JK, Harlan WR. The dissemination of new medical information. JAMA 1979; 241 (24) 2622-2624.
  • 28 Strasser TC. The information needs of practicing physicians in Northeastern New York State. J Med Libr Assoc 2012; 100 (Suppl. 04) G.
  • 29 Curry L, Putnam RW. Continuing medical education in Maritime Canada: The methods physicians use, would prefer and find most effective. Can Med Assoc J 1981; 124 (05) 563-566.
  • 30 Cohen SJ, Weinberger M, Mazzuca S, McDonald CJ. Perceived influence of different information sources on the decision-making of internal medicine house staff and faculty. Soc Sci Med 1982; 16 (14) 1361-1364.
  • 31 Northup DE, Moore-West M, Skipper B, Teaf SR. Characteristics of Clinical Information-Searching: Investigation Using Critical Incident Technique. J Med Educ 1983; 58 (11) 873-881.
  • 32 Kochen M, Cohen L, Wulff Y. Information systems and clinical research by residents in internal medicine. Methods Inf Med 1985; 24 (02) 85-90.
  • 33 Stinson ER, Mueller DA. Survey of health professionals’ information habits and needs. Conducted through personal interviews. JAMA 1980; 243 (02) 140-143.
  • 34 Christensen DB, Wertheimer AI. Sources of information and influence on new drug prescribing among physicians in an HMO. Soc Sci Med 1979; 13A (03) 313-322.
  • 35 Covell DG, Uman GC, Manning PR. Information needs in office practice: are they being met?. Ann Intern Med 1985; 103 (04) 596-599.
  • 36 Rudin R, Volk L, Simon S, Bates D. What Affects Clinicians’ Usage of Health Information Exchange?. Appl Clin Inform 2011; 2 (03) 250-262.
  • 37 Abdullah U, Ahmad J, Ahmed A. Analysis of effectiveness of apriori algorithm in medical billing data mining. Proceedings –4th IEEE International Conference on Emerging Technologies. 2008 ICET 2008: 327-331.
  • 38 Sharma N, Om H. Extracting Significant Patterns for Oral Cancer Detection Using Apriori Algorithm. Intell Inf Manag 2014; 6: 30-37.
  • 39 Ilayaraja M, Meyyappan T. Mining medical data to identify frequent diseases using Apriori algorithm. Proceedings of the 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, PRIME 2013: 194-199.
  • 40 Vest JR, Zhao H, Jasperson J, Jaspserson J, Gamm LD, Ohsfeldt RL. Factors motivating and affecting health information exchange usage. J Am Med Inform Assoc 2011; 18 (02) 143-149.
  • 41 Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. ACM SIGMOD Rec 1993; 22: 207-216.
  • 42 Hahsler M, Buchta BG, Hornik K. Arules: Mining Association Rules and Frequent item sets. 2015. https://cran.r-project.org/web/packages/arules/index.html .
  • 43 Alvarez SA. Chi-squared computation for association rules: preliminary results. Boston, MA: Boston College; 2003
  • 44 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995; 57 (01) 289-300.
  • 45 Smith K. Effective communication with primary care providers. Pediatr Clin North Am 2014; 61 (04) 671-679.
  • 46 Koopman RJ, Steege LMB, Moore JL, Clarke MA, Canfield SM, Kim MS, Belden JL. Physician Information Needs and Electronic Health Records (EHRs): Time to Reengineer the Clinic Note. J Am Board Fam Med 2015; 28 (03) 316-323.
  • 47 Landrigan CP, Conway PH, Edwards S, Srivastava R. Pediatric hospitalists: a systematic review of the literature. Pediatrics 2006; 117 (05) 1736-1744.
  • 48 Stewart BA, Fernandes S, Rodriguez-Huertas E, Landzberg M. A preliminary look at duplicate testing associated with lack of electronic health record interoperability for transferred patients. J Am Med Inform Assoc 2010; 17 (03) 341-344.
  • 49 Pantilat SZ, Lindenauer PK, Katz PP, Wachter RM. Primary care physician attitudes regarding communication with hospitalists. Am J Med 2001; 111 9B 15S-20S.
  • 50 Schabetsberger T, Ammenwerth E, Andreatta S, Gratl G, Haux R, Lechleitner G, Schindelwig K, Stark C, Vogl R, Wilhelmy I, Wozak F. From a paper-based transmission of discharge summaries to electronic communication in health care regions. Int J Med Inform 2006; 75 3–4 209-215.
  • 51 Laye PG. Tying up loose ends. Thermochim Acta 1997; 300 (01) 237-245.
  • 52 El-Kareh R, Roy C, Brodsky G, Perencevich M, Poon EG. Incidence and predictors of microbiology results returning postdischarge and requiring follow-up. J Hosp Med 2011; 6 (05) 291-296.
  • 53 Roy CL, Poon EC, Karson AS, Ladak-Merchant Z, Johnson RE, Maviglia SM, Gandhi TK. Patient safety concerns arising from test results that return after hospital discharge. Ann Intern Med 2005; 143 (02) 121-128.
  • 54 Walz SE, Smith M, Cox E, Sattin J, Kind AJH. Pending laboratory tests and the hospital discharge summary in patients discharged to sub-acute care. J Gen Intern Med 2011; 26 (04) 393-398.
  • 55 Van Walraven C, Laupacis A, Seth R, Wells G. Dictated versus database-generated discharge summaries: A randomized clinical trial. CMAJ 1999; 160 (03) 319-326.
  • 56 Teach RL, Shortliffe EH. An analysis of physician attitudes regarding computer-based clinical consultation systems. Comput Biomed Res 1981; 14 (06) 542-558.
  • 57 Singer J, Sacks HS, Lucente F, Chalmers TC. Physician attitudes toward applications of computer data base systems. JAMA 1983; 249 (12) 1610-1614.
  • 58 Campion TR, Edwards AM, Johnson SB, Kaushal R. Health information exchange system usage patterns in three communities: practice sites, users, patients, and data. Int J Med Inform 2013; 82 (Suppl. 09) 810-820.
  • 59 Johnson KB, Unertl KM, Chen Q, Lorenzi NM, Nian H, Bailey J, Frisse M. Health information exchange usage in emergency departments and clinics: the who, what, and why. J Am Med Inform Assoc 2011; 18 (05) 690-697.
  • 60 Ozkaynak M, Brennan PF. Revisiting sociotechnical systems in a case of unreported use of health information exchange system in three hospital emergency departments. J Eval Clin Pract 2013; 19 (02) 370-373.