Abstract
Health information technology (IT) systems have been demonstrated to improve the processes of care and outcomes related to chronic disease. Less than 25% of ambulatory practices were using electronic health records (EHRs) in 2004; however, this number increased to more than 80% a decade later. This expansion can be attributed to the Health Information Technology for Economic and Clinical Health Act (HITECH), which was designed to stimulate adoption of EHRs into the US healthcare system. HITECH promoted the “meaningful use” of EHRs in ways that would (1) electronically capture key patient health information, (2) use electronic patient information to facilitate clinical decision support, (3) facilitate reporting of quality measures to inform quality improvement efforts and to facilitate pay-for-performance reimbursement structures, (4) encourage patient self-management, and (5) improve transitions of care by facilitating sharing of patient information among treating providers. Sociotechnical factors are strong determinants for the successful implementation of health IT which involve end users in the implementation process, responsiveness to end-user feedback, adequate user training, and consideration of clinical workflow. The future success of health IT has less to do with advances in technology and more to do with viewing health IT as a key tool that needs to successfully integrate into clinical workflows.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
HealthIT.gov. Basics of health IT 2013. Available from: https://www.healthit.gov/patients-families/basics-health-it.
ONC. Office-based physician electronic health record adoption: 2004–2014. Washington, DC: The Office of the National Coordinator (ONC) for Health Information Technology; 2015. Available from: http://dashboard.healthit.gov/quickstats/pages/physician-ehr-adoption-trends.php.
Gillum RF. From papyrus to the electronic tablet: a brief history of the clinical medical record with lessons for the digital age. Am J Med. 2013;126(10):853–7.
Tang PC, McDonald CJ. Electronic health record systems. In: Shortliffe EH, Cimino JJ, editors. Biomedical informatics: computer applications in health care and biomedicine. 3rd ed: Springer Science, Berlin, Germany; 2006.
Devine EB, Wilson-Norton JL, Lawless NM, Hansen RN, Hazlet TK, Kelly K, et al. Characterization of prescribing errors in an internal medicine clinic. Am J Health Syst Pharm. 2007;64(10):1062–70.
Gandhi TK, Weingart SN, Seger AC, Borus J, Burdick E, Poon EG, et al. Outpatient prescribing errors and the impact of computerized prescribing. J Gen Intern Med. 2005;20(9):837–41.
Brodell RT, Helms SE, KrishnaRao I, Bredle DL. Prescription errors. Legibility and drug name confusion. Arch Fam Med. 1997;6(3):296–8.
Yarnall KS, Pollak KI, Ostbye T, Krause KM, Michener JL. Primary care: is there enough time for prevention? Am J Public Health. 2003;93(4):635–41.
Jaen CR, Stange KC, Nutting PA. Competing demands of primary care: a model for the delivery of clinical preventive services. J Fam Pract. 1994;38(2):166–71.
McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, et al. The quality of health care delivered to adults in the United States. N Engl J Med. 2003;348(26):2635–45.
IOM. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001.
Congress.gov. American recovery and reinvestment act of 2009. Washington, DC: Library of Congress; 2009. Available from: https://www.congress.gov/bill/111th-congress/house-bill/1.
HealthIT.gov. Health IT legislation and regulations 2016. Available from: https://www.healthit.gov/policy-researchers-implementers/health-it-legislation.
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(4):900–4.
Greenes RG. Clinical decision support: the road ahead. 1st ed. Academic Press, Cambridge, Massachusetts, USA; 2011.
Friedlin J, Dexter PR, Overhage JM. Details of a successful clinical decision support system. AMIA Annu Symp Proc. 2007:254–8.
Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.
Cimino JJ, Li J. Sharing infobuttons to resolve clinicians’ information needs. AMIA Annu Symp Proc. 2003;2003:815.
El-Sappagh S, El-Masri S. A distributed clinical decision support system architecture. J King Saud Univ – Comput Inf Sci. 2013;26(1):69–78.
ACC. 2013 Prevention guidelines ASCVD risk estimator: American College of Cardiology; 2013. Available from: http://www.acc.org/tools-and-practice-support/mobile-resources/features/2013-prevention-guidelines-ascvd-risk-estimator.
Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care. 2010;19(Suppl 3):i68–74.
IOM. To err is human: building a safer health system. Washington, DC: Institute of Medicine (IOM), National Academy Press; 1999.
Fathima M, Peiris D, Naik-Panvelkar P, Saini B, Armour CL. Effectiveness of computerized clinical decision support systems for asthma and chronic obstructive pulmonary disease in primary care: a systematic review. BMC Pulm Med. 2014;14:189.
Jeffery R, Iserman E, Haynes RB, Team CSR. Can computerized clinical decision support systems improve diabetes management? A systematic review and meta-analysis. Diabet Med. 2013;30(6):739–45.
Nies J, Colombet I, Degoulet P, Durieux P. Determinants of success for computerized clinical decision support systems integrated in CPOE systems: a systematic review. AMIA Annu Symp Proc. 2006;2006:594–8.
Roshanov PS, Misra S, Gerstein HC, Garg AX, Sebaldt RJ, Mackay JA, et al. Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review. Implement Sci. 2011;6:92.
Souza NM, Sebaldt RJ, Mackay JA, Prorok JC, Weise-Kelly L, Navarro T, et al. Computerized clinical decision support systems for primary preventive care: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implement Sci. 2011;6:87.
Nanji KC, Slight SP, Seger DL, Cho I, Fiskio JM, Redden LM, et al. Overrides of medication-related clinical decision support alerts in outpatients. J Am Med Inform Assoc. 2014;21(3):487–91.
Wennberg JE. Practice variation: implications for our health care system. Manag Care. 2004;13(9 Suppl):3–7.
RWJF. The value of personal health records and web portals to engage consumers and improve quality: Robert Wood Johnson Foundation; 2012. Available from: http://www.rwjf.org/en/library/research/2012/07/the-value-of-personal-health-records-and-web-portals-to-engage-c.html.
Krist AH, Woolf SH. A vision for patient-centered health information systems. JAMA. 2011;305(3):300–1.
Kern LM, Barron Y, Abramson EL, Patel V, Kaushal R, HEAL NY. Promoting interoperable health information technology in New York State. Health Aff (Millwood). 2009;28(2):493–504.
Adler-Milstein J, Bates DW, Jha AK. Operational health information exchanges show substantial growth, but long-term funding remains a concern. Health Aff (Millwood). 2013;32(8):1486–92.
Rudin RS, Motala A, Goldzweig CL, Shekelle PG. Usage and effect of health information exchange: a systematic review. Ann Intern Med. 2014;161(11):803–11.
Fontaine P, Ross SE, Zink T, Schilling LM. Systematic review of health information exchange in primary care practices. J Am Board Fam Med. 2010;23(5):655–70.
Dorr D, Bonner LM, Cohen AN, Shoai RS, Perrin R, Chaney E, et al. Informatics systems to promote improved care for chronic illness: a literature review. J Am Med Inform Assoc. 2007;14(2):156–63.
Research2Guidance. mHealth app developer economics 2016: the current status and trends of the mHealth market; 2016.
WeightWatchers. Weight watchers mobile: weight watchers; 2016. Available from: http://www.weightwatchers.com/templates/marketing/marketing_utool_1col.aspx?pageid=1191351.
Miller AS, Cafazzo JA, Seto E. A game plan: gamification design principles in mHealth applications for chronic disease management. Health Informatics J. 2016;22(2):184–93.
Fitocracy. Fitocracy: Fitocracy; 2016. Available from: https://www.fitocracy.com/.
FitnessKeeper. RunKeeper: FitnessKeeper; 2016. Available from: https://runkeeper.com/.
MangoHealth. Mango Health: Mango Health; 2016. Available from: https://www.mangohealth.com/.
mySugr. mySugr: mySugr; 2016. Available from: https://mysugr.com/.
Gee PM, Greenwood DA, Paterniti DA, Ward D, Miller LM. The eHealth enhanced chronic care model: a theory derivation approach. J Med Internet Res. 2015;17(4):e86.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Moore, C.R. (2018). Health Information Technology. In: Daaleman, T., Helton, M. (eds) Chronic Illness Care. Springer, Cham. https://doi.org/10.1007/978-3-319-71812-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-71812-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71811-8
Online ISBN: 978-3-319-71812-5
eBook Packages: MedicineMedicine (R0)