Technology Implementation Impacting the Outcomes of Patients with CAP

Abstract

Community-acquired pneumonia (CAP) combined with influenza is the eighth leading cause of death in the United States. This article examines the literature to understand and describe whether technology implementation has impacted the outcomes of patients with CAP. We conducted an electronic search of PubMed and scanned references of articles meeting inclusion criteria. Twenty-six articles were included in this review. We surveyed this literature for answers to the following questions: Can technology be used to improve quality of care and guideline compliance in CAP? How can we overcome the behavioral bottleneck that prevents adoption of computerized decision support systems? How reliable are our data in the era of electronic medical records? What are the risks associated with technology implementation? No articles demonstrated that technology implementation improves outcomes in the care of patients with CAP.

Keywords

pneumonia - technology - computerized decision support - electronic medical records

• References

• 1 Center for Health Statistics. Health, United States, April 24, 2008. http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr5610.pdf . Accessed June 7, 2010
• 2 Marston BJ, Plouffe JF, File Jr TM , et al; The Community-Based Pneumonia Incidence Study Group. Incidence of community-acquired pneumonia requiring hospitalization. Results of a population-based active surveillance Study in Ohio. Arch Intern Med 1997; 157 (15) 1709-1718
• 3 Niederman MS, McCombs JS, Unger AN, Kumar A, Popovian R. The cost of treating community-acquired pneumonia. Clin Ther 1998; 20 (4) 820-837
• 4 Dean NC, Jones JP, Aronsky D , et al. Hospital admission decision for patients with community-acquired pneumonia: variability among physicians in an emergency department. Ann Emerg Med 2012; 59 (1) 35-41
• 5 McMahon Jr LF, Wolfe RA, Tedeschi PJ. Variation in hospital admissions among small areas: a comparison of Maine and Michigan. Med Care 1989; 27 (6) 623-631
• 6 Rosenthal GE, Harper DL, Shah A, Covinsky KE. A regional evaluation of variation in low-severity hospital admissions. J Gen Intern Med 1997; 12 (7) 416-422
• 7 Shekelle PG, Morton SC, Keeler EB. Costs and Benefits of Health Information Technology. Evidence Report/Technology Assessment No. 132. (Prepared by the Southern California Evidence-based Practice Center under Contract No. 290–02–0003.) AHRQ Publication No. 06–E006 Rockville, MD: Agency for Healthcare Research and Quality; 2006
• 8 Filardo G, Nicewander D, Hamilton C , et al. A hospital-randomized controlled trial of an educational quality improvement intervention in rural and small community hospitals in Texas following implementation of information technology. Am J Med Qual 2007; 22 (6) 418-427
• 9 Filardo G, Nicewander D, Herrin J , et al. A hospital-randomized controlled trial of a formal quality improvement educational program in rural and small community Texas hospitals: one year results. Int J Qual Health Care 2009; 21 (4) 225-232
• 10 Jones SS, Adams JL, Schneider EC, Ringel JS, McGlynn EA. Electronic health record adoption and quality improvement in US hospitals. Am J Manag Care 2010; 16 (12, Suppl HIT) SP64-SP71
• 11 Himmelstein DU, Wright A, Woolhandler S. Hospital computing and the costs and quality of care: a national study. Am J Med 2010; 123 (1) 40-46
• 12 Weiner SG, Brown SF, Goetz JD, Webber CA. Weekly e-mail reminders influence emergency physician behavior: a case study using the Joint Commission and Centers for Medicare and Medicaid Services Pneumonia Guidelines. Acad Emerg Med 2009; 16 (7) 626-631
• 13 Buising KL, Thursky KA, Black JF , et al. Improving antibiotic prescribing for adults with community acquired pneumonia: Does a computerised decision support system achieve more than academic detailing alone?—a time series analysis. BMC Med Inform Decis Mak 2008; 8: 35-44
• 14 Stevenson KB, Barbera J, Moore JW, Samore MH, Houck P. Understanding keys to successful implementation of electronic decision support in rural hospitals: analysis of a pilot study for antimicrobial prescribing. Am J Med Qual 2005; 20 (6) 313-318
• 15 Flanagan JR, Peterson M, Dayton C , et al. Email recruitment to use web decision support tools for pneumonia. Proc AMIA Symp 2002; 255-259
• 16 Kawahara NE, Jordan FM. Influencing prescribing behavior by adapting computerized order-entry pathways. Am J Hosp Pharm 1989; 46 (9) 1798-1801
• 17 McAlearney AS, Chisolm D, Veneris S, Rich D, Kelleher K. Utilization of evidence-based computerized order sets in pediatrics. Int J Med Inform 2006; 75 (7) 501-512
• 18 Westphal JF, Jehl F, Javelot H, Nonnenmacher C. Enhanced physician adherence to antibiotic use guidelines through increased availability of guidelines at the time of drug ordering in hospital setting. Pharmacoepidemiol Drug Saf 2011; 20 (2) 162-168
• 19 Jha AK, Orav EJ, Ridgway AB, Zheng J, Epstein AM. Does the Leapfrog program help identify high-quality hospitals?. Jt Comm J Qual Patient Saf 2008; 34 (6) 318-325
• 20 Aronsky D, Haug PJ. An integrated decision support system for diagnosing and managing patients with community-acquired pneumonia. Proc AMIA Symp 1999; 197-201
• 21 Aronsky D, Haug PJ. Diagnosing community-acquired pneumonia with a Bayesian network. Proc AMIA Symp 1998; 632-636
• 22 Aronsky D, Haug PJ. Automatic identification of patients eligible for a pneumonia guideline. Proc AMIA Symp 2000; 12-16
• 23 Lagor C, Aronsky D, Fiszman M, Haug PJ. Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models. Stud Health Technol Inform 2001; 84 (Pt 1) 493-497
• 24 Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. Automatic detection of acute bacterial pneumonia from chest x-ray reports. J Am Med Inform Assoc 2000; 7 (6) 593-604
• 25 Niemi K, Geary S, Quinn B, Larrabee M, Brown K. Implementation and evaluation of electronic clinical decision support for compliance with pneumonia and heart failure quality indicators. Am J Health Syst Pharm 2009; 66 (4) 389-397
• 26 Aronsky D, Haug PJ, Lagor C, Dean NC. Accuracy of administrative data for identifying patients with pneumonia. Am J Med Qual 2005; 20 (6) 319-328
• 27 Whittle J, Fine MJ, Joyce DZ , et al. Community-acquired pneumonia: can it be defined with claims data?. Am J Med Qual 1997; 12 (4) 187-193
• 28 Yu O, Nelson JC, Bounds L, Jackson LA. Classification algorithms to improve the accuracy of identifying patients hospitalized with community-acquired pneumonia using administrative data. Epidemiol Infect 2011; 139 (9) 1296-1306
• 29 Hripcsak G, Knirsch C, Zhou L, Wilcox A, Melton GB. Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia. Comput Biol Med 2007; 37 (3) 296-304
• 30 Hripcsak G, Knirsch C, Zhou L, Wilcox A, Melton G. Bias associated with mining electronic health records. J Biomed Discov Collab 2011; 6: 48-52
• 31 Aronsky D, Haug PJ. Assessing the quality of clinical data in a computer-based record for calculating the pneumonia severity index. J Am Med Inform Assoc 2000; 7 (1) 55-65
• 32 Jones BE, Jones J, Bewick T , et al. CURB-65 pneumonia severity assessment adapted for electronic decision support. Chest 2011; 140 (1) 156-163
• 33 Graham TA, Kushniruk AW, Bullard MJ, Holroyd BR, Meurer DP, Rowe BH. How usability of a web-based clinical decision support system has the potential to contribute to adverse medical events. AMIA Annu Symp Proc 2008; 257-261